Managing Brute Force Attacks in a Distributed Storage System

ABSTRACT

A method for execution in a storage network begins by performing a key derivation function on a password to produce a key and issuing a set of passwords based on the key to a set of storage units, where a data object is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices and the set of encoded data slices are stored in the set of storage units. The method continues, by receiving at least a decode threshold number of confidential information responses from the set of storage units and regenerating a set of keys for the confidential information responses. The method then continues by decrypting the confidential information responses using the set of keys to reproduce a set of encoded data slices and dispersed storage error decoding a decode threshold number of the set of encoded data slices to produce recovered data.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. § 120 as a continuation of U.S. Utility application Ser. No. 17/663,566, entitled “PERFORMANCE AIDED DATA MIGRATION IN A DISTRIBUTED STORAGE NETWORK”, filed May 16, 2022, which is a continuation-in-part of U.S. Utility application Ser. No. 16/855,395, entitled “BLINDED PASSWORDS FOR A DISTRIBUTED STORAGE SYSTEM”, filed Apr. 22, 2020, issued as U.S. Pat. No. 11,336,428 on which is a continuation of U.S. Utility application Ser. No. 16/053,466, entitled “LIMITING BRUTE FORCE ATTACKS AGAINST DISPERSED CREDENTIALS IN A DISTRIBUTED STORAGE SYSTEM”, filed Aug. 2, 2018, issued as U.S. Pat. No. 10,644,874 on May 5, 2020, which is a continuation-in-part of U.S. Utility application Ser. No. 15/827,073, entitled “MIGRATING DATA IN A DISTRIBUTED STORAGE NETWORK”, filed Nov. 30, 2017, issued as U.S. Pat. No. 10,089,036 on Oct. 2, 2018, which is a continuation-in-part of U.S. Utility application Ser. No. 14/721,838, entitled “ADJUSTING TIMING OF STORING DATA IN A DISPERSED STORAGE NETWORK”, filed May 26, 2015, issued as U.S. Pat. No. 9,841,925 on Dec. 12, 2017, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/031,342, entitled “DISPERSED STORAGE NETWORK STORAGE RETRY MECHANISM”, filed Jul. 31, 2014, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility patent application for all purposes.

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BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and more particularly to dispersed storage of data and distributed task processing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or online purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc., on an Internet storage system. The Internet storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and task processing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing in accordance with the present invention;

FIG. 5 is a logic diagram of an example of a method for outbound DST processing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersed error encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of the dispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding in accordance with the present invention;

FIG. 9 is a diagram of an example of grouping selection processing of the outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groups in accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST execution unit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DST execution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing in accordance with the present invention;

FIG. 14 is a logic diagram of an example of a method for inbound DST processing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processing of the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersed error decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of the dispersed error decoding in accordance with the present invention;

FIG. 18 is a diagram of an example of a de-segment processing of the dispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into data in accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within the distributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation of outbound distributed storage and/or task (DST) processing for storing data in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed error encoding for the example of FIG. 21 in accordance with the present invention;

FIG. 23 is a diagram of an example of converting data into pillar slice groups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storage operation of a DST execution unit in accordance with the present invention;

FIG. 25 is a schematic block diagram of an example of operation of inbound distributed storage and/or task (DST) processing for retrieving dispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed error decoding for the example of FIG. 25 in accordance with the present invention;

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing a plurality of data and a plurality of task codes in accordance with the pre sent invention;

FIG. 28 is a schematic block diagram of an example of the distributed computing system performing tasks on stored data in accordance with the present invention;

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module facilitating the example of FIG. 28 in accordance with the present invention;

FIG. 30 is a diagram of a specific example of the distributed computing system performing tasks on stored data in accordance with the present invention;

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30 in accordance with the present invention;

FIG. 32 is a diagram of an example of DST allocation information for the example of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performing the example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information into final results for the example of FIG. 30 in accordance with the present invention;

FIG. 40A is a schematic block diagram of an embodiment of a decentralized agreement module in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of selecting the resource in accordance with the present invention;

FIG. 40C is a schematic block diagram of an embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 40D is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory in accordance with the present invention;

FIGS. 41A and 41C are a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 41B is a schematic block diagram of an embodiment of an access unit in accordance with the present invention;

FIG. 41D is a flowchart illustrating an example of adjusting timing of storing data in accordance with the present invention;

FIG. 42A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 42B is a flowchart illustrating an example of accessing stored data in accordance with the present invention;

FIG. 43A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 43B is a flowchart illustrating an example of migrating data from a first storage format to a second storage format in accordance with the present invention;

FIG. 44A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 44B is a flowchart illustrating an example of recovering data in accordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 45B is a flowchart illustrating an example of migrating data from a first storage pool tier level to a second storage pool tier level in accordance with the present invention;

FIG. 46A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 46B is a flowchart illustrating an example of accessing data within a vault storage container of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 47A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 47B is a flowchart illustrating an example of selecting a set of memory devices in accordance with the present invention;

FIG. 48A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 48B is a flowchart illustrating an example of updating a dispersed hierarchical index in accordance with the present invention; and

FIG. 49 is a flowchart illustrating an example of synchronizing a dispersed hierarchical index with stored data in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system 10 that includes a user device 12 and/or a user device 14, a distributed storage and/or task (DST) processing unit 16, a distributed storage and/or task network (DSTN) managing unit 18, a DST integrity processing unit and a distributed storage and/or task network (DSTN) module 22. The components of the distributed computing system 10 are coupled via a network 24, which may include one or more wireless and/or wire lined communication systems; one or more private intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/or task (DST) execution units 36 that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.). Each of the DST execution units is operable to store dispersed error encoded data and/or to execute, in a distributed manner, one or more tasks on data. The tasks may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTN managing unit 18, and the DST integrity processing unit 20 include a computing core 26 and may be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a personal computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. User device 12 and DST processing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includes software and/or hardware to support one or more communication links via the network 24 indirectly and/or directly. For example, interfaces 30 support a communication link (e.g., wired, wireless, direct, via a LAN, via the network 24, etc.) between user device 14 and the DST processing unit 16. As another example, interface 32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network 24) between user device 12 and the DSTN module 22 and between the DST processing unit 16 and the DSTN module 22. As yet another example, interface 33 supports a communication link for each of the DSTN managing unit 18 and DST integrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersed storage (DS) error encoded data storage and retrieval, to support distributed task processing on received data, and/or to support distributed task processing on stored data. In general and with respect to DS error encoded data storage and retrieval, the distributed computing system 10 supports three primary operations: storage management, data storage and retrieval (an example of which will be discussed with reference to FIGS. 20-26 ), and data storage integrity verification. In accordance with these three primary functions, data can be encoded, distributedly stored in physically different locations, and subsequently retrieved in a reliable and secure manner. Such a system is tolerant of a significant number of failures (e.g., up to a failure level, which may be greater than or equal to a pillar width minus a decode threshold minus one) that may result from individual storage device failures and/or network equipment failures without loss of data and without the need for a redundant or backup copy. Further, the system allows the data to be stored for an indefinite period of time without data loss and does so in a secure manner (e.g., the system is very resistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage and retrieval) begins and ends with a user device 12-14. For instance, if a second type of user device 14 has data 40 to store in the DSTN module 22, it sends the data 40 to the DST processing unit 16 via its interface 30. The interface 30 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). In addition, the interface 30 may attach a user identification code (ID) to the data 40.

To support storage management, the DSTN managing unit 18 performs DS management services. One such DS management service includes the DSTN managing unit 18 establishing distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for a user device 12-14 individually or as part of a group of user devices. For example, the DSTN managing unit 18 coordinates creation of a vault (e.g., a virtual memory block) within memory of the DSTN module 22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The DSTN managing unit 18 may facilitate storage of DS error encoding parameters for each vault of a plurality of vaults by updating registry information for the distributed computing system 10. The facilitating includes storing updated registry information in one or more of the DSTN module 22, the user device 12, the DST processing unit 16, and the DST integrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSTN module 22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the DSTN managing unit 18 tracks the number of times a user accesses a private vault and/or public vaults, which can be used to generate a per-access billing information. In another instance, the DSTN managing unit 18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate a per-data-amount billing information.

Another DS management service includes the DSTN managing unit 18 performing network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system 10, and/or establishing authentication credentials for DST execution units 36. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the system 10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the system 10.

To support data storage integrity verification within the distributed computing system 10, the DST integrity processing unit 20 performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, the DST integrity processing unit 20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSTN module 22. For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in memory of the DSTN module 22. Note that the DST integrity processing unit 20 may be a separate unit as shown, it may be included in the DSTN module 22, it may be included in the DST processing unit 16, and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task processing) management and DST execution on received data (an example of which will be discussed with reference to FIGS. 3-19 ). With respect to the storage portion of the DST management, the DSTN managing unit 18 functions as previously described. With respect to the tasking processing of the DST management, the DSTN managing unit 18 performs distributed task processing (DTP) management services. One such DTP management service includes the DSTN managing unit 18 establishing DTP parameters (e.g., user-vault affiliation information, billing information, user-task information, etc.) for a user device 12-14 individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18 performing DTP network operations, network administration (which is essentially the same as described above), and/or network maintenance (which is essentially the same as described above). Network operations include, but are not limited to, authenticating user task processing requests (e.g., valid request, valid user, etc.), authenticating results and/or partial results, establishing DTP authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system, and/or establishing DTP authentication credentials for DST execution units.

To support distributed task processing on stored data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task) management and DST execution on stored data. With respect to the DST execution on stored data, if the second type of user device 14 has a task request 38 for execution by the DSTN module 22, it sends the task request 38 to the DST processing unit 16 via its interface 30. An example of DST execution on stored data will be discussed in greater detail with reference to FIGS. 27-39 . With respect to the DST management, it is substantially similar to the DST management to support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core 26 that includes a processing module 50, a memory controller 52, main memory 54, a video graphics processing unit 55, an input/output (IO) controller 56, a peripheral component interconnect (PCI) interface 58, an 10 interface module 60, at least one IO device interface module 62, a read only memory (ROM) basic input output system (BIOS) 64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module 66, a host bus adapter (HBA) interface module 68, a network interface module 70, a flash interface module 72, a hard drive interface module 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSTN interface module 76 and/or the network interface module 70 may function as the interface 30 of the user device 14 of FIG. 1 . Further note that the IO device interface module 62 and/or the memory interface modules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing system performing a distributed storage and task processing operation. The distributed computing system includes a DST (distributed storage and/or task) client module 34 (which may be in user device 14 and/or in DST processing unit 16 of FIG. 1 ), a network 24, a plurality of DST execution units 1-n that includes two or more DST execution units 36 of FIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1 ), a DST managing module (not shown), and a DST integrity verification module (not shown). The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. Each of the DST execution units 1-n includes a controller 86, a processing module 84, memory 88, a DT (distributed task) execution module 90, and a DST client module 34.

In an example of operation, the DST client module 34 receives data 92 and one or more tasks 94 to be performed upon the data 92. The data 92 may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or task(s) (e.g., MIPS intensive), distributed processing of the task(s) on the data is desired. For example, the data 92 may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80 receives the data 92 and the task(s) 94. The outbound DST processing section 80 processes the data 92 to produce slice groupings 96. As an example of such processing, the outbound DST processing section 80 partitions the data 92 into a plurality of data partitions. For each data partition, the outbound DST processing section 80 dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping 96. In addition, the outbound DST processing section 80 partitions the task 94 into partial tasks 98, where the number of partial tasks 98 may correspond to the number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24, the slice groupings 96 and the partial tasks 98 to the DST execution units 1-n of the DSTN module 22 of FIG. 1 . For example, the outbound DST processing section 80 sends slice group 1 and partial task 1 to DST execution unit 1. As another example, the outbound DST processing section 80 sends slice group #n and partial task #n to DST execution unit #n.

Each DST execution unit performs its partial task 98 upon its slice group 96 to produce partial results 102. For example, DST execution unit #1 performs partial task #1 on slice group #1 to produce a partial result #1, for results. As a more specific example, slice group #1 corresponds to a data partition of a series of digital books and the partial task #1 corresponds to searching for specific phrases, recording where the phrase is found, and establishing a phrase count. In this more specific example, the partial result #1 includes information as to where the phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, the DST execution units send, via the network 24, their partial results 102 to the inbound DST processing section 82 of the DST client module 34. The inbound DST processing section 82 processes the received partial results 102 to produce a result 104. Continuing with the specific example of the preceding paragraph, the inbound DST processing section 82 combines the phrase count from each of the DST execution units 36 to produce a total phrase count. In addition, the inbound DST processing section 82 combines the ‘where the phrase was found’ information from each of the DST execution units 36 within their respective data partitions to produce ‘where the phrase was found’ information for the series of digital books.

In another example of operation, the DST client module 34 requests retrieval of stored data within the memory of the DST execution units 36 (e.g., memory of the DSTN module). In this example, the task 94 is retrieve data stored in the memory of the DSTN module. Accordingly, the outbound DST processing section 80 converts the task 94 into a plurality of partial tasks 98 and sends the partial tasks 98 to the respective DST execution units 1-n.

In response to the partial task 98 of retrieving stored data, a DST execution unit 36 identifies the corresponding encoded data slices 100 and retrieves them. For example, DST execution unit #1 receives partial task #1 and retrieves, in response thereto, retrieved slices #1. The DST execution units 36 send their respective retrieved slices 100 to the inbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100 into data 92. For example, the inbound DST processing section 82 de-groups the retrieved slices 100 to produce encoded slices per data partition. The inbound DST processing section 82 then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DST processing section 82 de-partitions the data partitions to recapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., a plurality of n DST execution units 36) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 partitions data 92 into a plurality of data partitions 120. The number of partitions and the size of the partitions may be selected by the control module 116 via control 160 based on the data 92 (e.g., its size, its content, etc.), a corresponding task 94 to be performed (e.g., simple, complex, single step, multiple steps, etc.), DS encoding parameters (e.g., pillar width, decode threshold, write threshold, segment security parameters, slice security parameters, etc.), capabilities of the DST execution units 36 (e.g., processing resources, availability of processing recourses, etc.), and/or as may be inputted by a user, system administrator, or other operator (human or automated). For example, the data partitioning module 110 partitions the data 92 (e.g., 100 Terabytes) into 100,000 data segments, each being 1 Gigabyte in size. Alternatively, the data partitioning module 110 partitions the data 92 into a plurality of data segments, where some of data segments are of a different size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in a serial manner, a parallel manner, and/or a combination thereof. For each data partition 120, the DS error encoding module 112 DS error encodes the data partition 120 in accordance with control information 160 from the control module 116 to produce encoded data slices 122. The DS error encoding includes segmenting the data partition into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.). The control information 160 indicates which steps of the DS error encoding are active for a given data partition and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a data partition into a set of slice groupings 96. The number of slice groupings corresponds to the number of DST execution units 36 identified for a particular task 94. For example, if five DST execution units 36 are identified for the particular task 94, the group selecting module groups the encoded slices 122 of a data partition into five slice groupings 96. The grouping selector module 114 outputs the slice groupings 96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 and converts the task 94 into a set of partial tasks 98. For example, the distributed task control module 118 receives a task to find where in the data (e.g., a series of books) a phrase occurs and a total count of the phrase usage in the data. In this example, the distributed task control module 118 replicates the task 94 for each DST execution unit 36 to produce the partial tasks 98. In another example, the distributed task control module 118 receives a task to find where in the data a first phrase occurs, where in the data a second phrase occurs, and a total count for each phrase usage in the data. In this example, the distributed task control module 118 generates a first set of partial tasks 98 for finding and counting the first phase and a second set of partial tasks for finding and counting the second phrase. The distributed task control module 118 sends respective first and/or second partial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbound distributed storage and task (DST) processing that begins at step 126 where a DST client module receives data and one or more corresponding tasks. The method continues at step 128 where the DST client module determines a number of DST units to support the task for one or more data partitions. For example, the DST client module may determine the number of DST units to support the task based on the size of the data, the requested task, the content of the data, a predetermined number (e.g., user indicated, system administrator determined, etc.), available DST units, capability of the DST units, and/or any other factor regarding distributed task processing of the data. The DST client module may select the same DST units for each data partition, may select different DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determines processing parameters of the data based on the number of DST units selected for distributed task processing. The processing parameters include data partitioning information, DS encoding parameters, and/or slice grouping information. The data partitioning information includes a number of data partitions, size of each data partition, and/or organization of the data partitions (e.g., number of data blocks in a partition, the size of the data blocks, and arrangement of the data blocks). The DS encoding parameters include segmenting information, segment security information, error encoding information (e.g., dispersed storage error encoding function parameters including one or more of pillar width, decode threshold, write threshold, read threshold, generator matrix), slicing information, and/or per slice security information. The slice grouping information includes information regarding how to arrange the encoded data slices into groups for the selected DST units. As a specific example, if the DST client module determines that five DST units are needed to support the task, then it determines that the error encoding parameters include a pillar width of five and a decode threshold of three.

The method continues at step 132 where the DST client module determines task partitioning information (e.g., how to partition the tasks) based on the selected DST units and data processing parameters. The data processing parameters include the processing parameters and DST unit capability information. The DST unit capability information includes the number of DT (distributed task) execution units, execution capabilities of each DT execution unit (e.g., MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or and the other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.)), and/or any information germane to executing one or more tasks.

The method continues at step 134 where the DST client module processes the data in accordance with the processing parameters to produce slice groupings. The method continues at step 136 where the DST client module partitions the task based on the task partitioning information to produce a set of partial tasks. The method continues at step 138 where the DST client module sends the slice groupings and the corresponding partial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersed storage (DS) error encoding module 112 of an outbound distributed storage and task (DST) processing section. The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives a data partition 120 from a data partitioning module and receives segmenting information as the control information 160 from the control module 116. The segmenting information indicates how the segment processing module 142 is to segment the data partition 120. For example, the segmenting information indicates how many rows to segment the data based on a decode threshold of an error encoding scheme, indicates how many columns to segment the data into based on a number and size of data blocks within the data partition 120, and indicates how many columns to include in a data segment 152. The segment processing module 142 segments the data 120 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., cyclic redundancy check (CRC), etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it may compress a data segment 152, encrypt the compressed data segment, and generate a CRC value for the encrypted data segment to produce a secure data segment 154. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments 154 in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters (e.g., also referred to as dispersed storage error coding parameters) include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an online coding algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordance with the pillar width of the error correction encoding parameters received as control information 160. For example, if the pillar width is five, the slicing module 148 slices an encoded data segment 156 into a set of five encoded data slices. As such, for a plurality of encoded data segments 156 for a given data partition, the slicing module outputs a plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice 158 based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it compresses an encoded data slice 158, encrypts the compressed encoded data slice, and generates a CRC value for the encrypted encoded data slice to produce a secure encoded data slice 122. When the per slice security processing module 150 is not enabled, it passes the encoded data slices 158 or is bypassed such that the encoded data slices 158 are the output of the DS error encoding module 112. Note that the control module 116 may be omitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersed storage (DS) error encoding module. In this example, a segment processing module 142 receives a data partition 120 that includes 45 data blocks (e.g., d1-d45), receives segmenting information (i.e., control information 160) from a control module, and segments the data partition 120 in accordance with the control information 160 to produce data segments 152. Each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data. As previously mentioned, the segmenting information indicates how many rows to segment the data partition into, indicates how many columns to segment the data partition into, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme is three; as such the number of rows to divide the data partition into is three. The number of columns for each row is set to 15, which is based on the number and size of data blocks. The data blocks of the data partition are arranged in rows and columns in a sequential order (i.e., the first row includes the first 15 data blocks; the second row includes the second 15 data blocks; and the third row includes the last 15 data blocks).

With the data blocks arranged into the desired sequential order, they are divided into data segments based on the segmenting information. In this example, the data partition is divided into 8 data segments; the first 7 include 2 columns of three rows and the last includes 1 column of three rows. Note that the first row of the 8 data segments is in sequential order of the first 15 data blocks; the second row of the 8 data segments in sequential order of the second 15 data blocks; and the third row of the 8 data segments in sequential order of the last 15 data blocks. Note that the number of data blocks, the grouping of the data blocks into segments, and size of the data blocks may vary to accommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding processing the data segments of FIG. 7 . In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words for encoding: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148 convert each data segment into a set of encoded data slices in accordance with error correction encoding parameters as control information 160. More specifically, when the error correction encoding parameters indicate a unity matrix Reed-Solomon based encoding algorithm, 5 pillars, and decode threshold of 3, the first three encoded data slices of the set of encoded data slices for a data segment are substantially similar to the corresponding word of the data segment. For instance, when the unity matrix Reed-Solomon based encoding algorithm is applied to data segment 1, the content of the first encoded data slice (DS1_d1&2) of the first set of encoded data slices (e.g., corresponding to data segment 1) is substantially similar to content of the first word (e.g., d1 & d2); the content of the second encoded data slice (DS1_d16&17) of the first set of encoded data slices is substantially similar to content of the second word (e.g., d16 & d17); and the content of the third encoded data slice (DS1_d31&32) of the first set of encoded data slices is substantially similar to content of the third word (e.g., d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the first set of encoded data slices include error correction data based on the first—third words of the first data segment. With such an encoding and slicing scheme, retrieving any three of the five encoded data slices allows the data segment to be accurately reconstructed.

The encoding and slices of data segments 2-7 yield sets of encoded data slices similar to the set of encoded data slices of data segment 1. For instance, the content of the first encoded data slice (DS2_d3&4) of the second set of encoded data slices (e.g., corresponding to data segment 2) is substantially similar to content of the first word (e.g., d3 & d4); the content of the second encoded data slice (DS2_d18&19) of the second set of encoded data slices is substantially similar to content of the second word (e.g., d18 & d19); and the content of the third encoded data slice (DS2_d33&34) of the second set of encoded data slices is substantially similar to content of the third word (e.g., d33 & d34). The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the second set of encoded data slices includes error correction data based on the first-third words of the second data segment.

FIG. 9 is a diagram of an example of grouping selection processing of an outbound distributed storage and task (DST) processing in accordance with group selection information as control information 160 from a control module. Encoded slices for data partition 122 are grouped in accordance with the control information 160 to produce slice groupings 96. In this example, a grouping selection module 114 organizes the encoded data slices into five slice groupings (e.g., one for each DST execution unit of a distributed storage and task network (DSTN) module). As a specific example, the grouping selection module 114 creates a first slice grouping for a DST execution unit #1, which includes first encoded slices of each of the sets of encoded slices. As such, the first DST execution unit receives encoded data slices corresponding to data blocks 1-15 (e.g., encoded data slices of contiguous data).

The grouping selection module 114 also creates a second slice grouping for a DST execution unit #2, which includes second encoded slices of each of the sets of encoded slices. As such, the second DST execution unit receives encoded data slices corresponding to data blocks 16-30. The grouping selection module 114 further creates a third slice grouping for DST execution unit #3, which includes third encoded slices of each of the sets of encoded slices. As such, the third DST execution unit receives encoded data slices corresponding to data blocks 31-45.

The grouping selection module 114 creates a fourth slice grouping for DST execution unit #4, which includes fourth encoded slices of each of the sets of encoded slices. As such, the fourth DST execution unit receives encoded data slices corresponding to first error encoding information (e.g., encoded data slices of error coding (EC) data). The grouping selection module 114 further creates a fifth slice grouping for DST execution unit #5, which includes fifth encoded slices of each of the sets of encoded slices. As such, the fifth DST execution unit receives encoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slice groups that expands on the preceding figures. As shown, the data 92 is partitioned in accordance with a partitioning function 164 into a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunkset of data) is encoded and grouped into slice groupings as previously discussed by an encoding and grouping function 166. For a given data partition, the slice groupings are sent to distributed storage and task (DST) execution units. From data partition to data partition, the ordering of the slice groupings to the DST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DST execution units such that the first DST execution receives first encoded data slices of each of the sets of encoded data slices, which corresponds to a first continuous data chunk of the first data partition (e.g., refer to FIG. 9 ), a second DST execution receives second encoded data slices of each of the sets of encoded data slices, which corresponds to a second continuous data chunk of the first data partition, etc.

For the second data partition, the slice groupings may be sent to the DST execution units in a different order than was done for the first data partition. For instance, the first slice grouping of the second data partition (e.g., slice group 2_1) is sent to the second DST execution unit; the second slice grouping of the second data partition (e.g., slice group 2_2) is sent to the third DST execution unit; the third slice grouping of the second data partition (e.g., slice group 2_3) is sent to the fourth DST execution unit; the fourth slice grouping of the second data partition (e.g., slice group 2_4, which includes first error coding information) is sent to the fifth DST execution unit; and the fifth slice grouping of the second data partition (e.g., slice group 2_5, which includes second error coding information) is sent to the first DST execution unit.

The pattern of sending the slice groupings to the set of DST execution units may vary in a predicted pattern, a random pattern, and/or a combination thereof from data partition to data partition. In addition, from data partition to data partition, the set of DST execution units may change. For example, for the first data partition, DST execution units 1-5 may be used; for the second data partition, DST execution units 6-10 may be used; for the third data partition, DST execution units 3-7 may be used; etc. As is also shown, the task is divided into partial tasks that are sent to the DST execution units in conjunction with the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST (distributed storage and/or task) execution unit that includes an interface 169, a controller 86, memory 88, one or more DT (distributed task) execution modules 90, and a DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution module receives a slice grouping 96 (e.g., slice group #1) via interface 169. The slice grouping 96 includes, per partition, encoded data slices of contiguous data or encoded data slices of error coding (EC) data. For slice group #1, the DST execution module receives encoded data slices of contiguous data for partitions #1 and #x (and potentially others between 3 and x) and receives encoded data slices of EC data for partitions #2 and #3 (and potentially others between 3 and x). Examples of encoded data slices of contiguous data and encoded data slices of error coding (EC) data are discussed with reference to FIG. 9 . The memory 88 stores the encoded data slices of slice groupings 96 in accordance with memory control information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on a partial task(s) 98 and distributed computing information (e.g., user information (e.g., user ID, distributed computing permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, temporary storage for task processing, etc.), task validation information, etc.). For example, the controller 86 interprets the partial task(s) 98 in light of the distributed computing information to determine whether a requestor is authorized to perform the task 98, is authorized to access the data, and/or is authorized to perform the task on this particular data. When the requestor is authorized, the controller 86 determines, based on the task 98 and/or another input, whether the encoded data slices of the slice grouping 96 are to be temporarily stored or permanently stored. Based on the foregoing, the controller 86 generates the memory control information 174 to write the encoded data slices of the slice grouping 96 into the memory 88 and to indicate whether the slice grouping 96 is permanently stored or temporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86 facilitates execution of the partial task(s) 98. In an example, the controller 86 interprets the partial task 98 in light of the capabilities of the DT execution module(s) 90. The capabilities include one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, etc. If the controller 86 determines that the DT execution module(s) have sufficient capabilities, it generates task control information 176.

The task control information 176 may be a generic instruction (e.g., perform the task on the stored slice grouping) or a series of operational codes. In the former instance, the DT execution module 90 includes a co-processor function specifically configured (fixed or programmed) to perform the desired task 98. In the latter instance, the DT execution module 90 includes a general processor topology where the controller stores an algorithm corresponding to the particular task 98. In this instance, the controller 86 provides the operational codes (e.g., assembly language, source code of a programming language, object code, etc.) of the algorithm to the DT execution module 90 for execution.

Depending on the nature of the task 98, the DT execution module 90 may generate intermediate partial results 102 that are stored in the memory 88 or in a cache memory (not shown) within the DT execution module 90. In either case, when the DT execution module 90 completes execution of the partial task 98, it outputs one or more partial results 102. The partial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of the DT execution module(s) 90 can support the partial task 98, the controller 86 determines that the DT execution module(s) 90 cannot adequately support the task 98 (e.g., does not have the right resources, does not have sufficient available resources, available resources would be too slow, etc.), it then determines whether the partial task 98 should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fully offloaded, it generates DST control information 178 and provides it to the DST client module 34. The DST control information 178 includes the partial task 98, memory storage information regarding the slice grouping 96, and distribution instructions. The distribution instructions instruct the DST client module 34 to divide the partial task 98 into sub-partial tasks 172, to divide the slice grouping 96 into sub-slice groupings 170, and identify other DST execution units. The DST client module 34 functions in a similar manner as the DST client module 34 of FIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slice groupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partial results), via the interface 169, from the DST execution units to which the task was offloaded. The DST client module 34 provides the sub-partial results to the DST execution unit, which processes the sub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should be partially offloaded, it determines what portion of the task 98 and/or slice grouping 96 should be processed locally and what should be offloaded. For the portion that is being locally processed, the controller 86 generates task control information 176 as previously discussed. For the portion that is being offloaded, the controller 86 generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g., sub-partial results) from the DST executions units to which a portion of the task was offloaded, it provides the sub-partial results to the DT execution module 90. The DT execution module 90 processes the sub-partial results with the sub-partial results it created to produce the partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of stored slices 100, stored results 104, partial results 102 when the DT execution module 90 stores partial results 102 and/or results 104 and the memory 88. For example, when the partial task 98 includes a retrieval request, the controller 86 outputs the memory control 174 to the memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of a distributed storage and task (DST) execution unit storing encoded data slices and executing a task thereon. To store the encoded data slices of a partition 1 of slice grouping 1, a controller 86 generates write commands as memory control information 174 such that the encoded slices are stored in desired locations (e.g., permanent or temporary) within memory 88.

Once the encoded slices are stored, the controller 86 provides task control information 176 to a distributed task (DT) execution module 90. As a first step of executing the task in accordance with the task control information 176, the DT execution module 90 retrieves the encoded slices from memory 88. The DT execution module 90 then reconstructs contiguous data blocks of a data partition. As shown for this example, reconstructed contiguous data blocks of data partition 1 include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module 90 performs the task on the reconstructed contiguous data blocks. For example, the task may be to search the reconstructed contiguous data blocks for a particular word or phrase, identify where in the reconstructed contiguous data blocks the particular word or phrase occurred, and/or count the occurrences of the particular word or phrase on the reconstructed contiguous data blocks. The DST execution unit continues in a similar manner for the encoded data slices of other partitions in slice grouping 1. Note that with using the unity matrix error encoding scheme previously discussed, if the encoded data slices of contiguous data are uncorrupted, the decoding of them is a relatively straightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (or missing), it can be rebuilt by accessing other DST execution units that are storing the other encoded data slices of the set of encoded data slices of the corrupted encoded data slice. In this instance, the DST execution unit having the corrupted encoded data slices retrieves at least three encoded data slices (of contiguous data and of error coding data) in the set from the other DST execution units (recall for this example, the pillar width is 5 and the decode threshold is 3). The DST execution unit decodes the retrieved data slices using the DS error encoding parameters to recapture the corresponding data segment. The DST execution unit then re-encodes the data segment using the DS error encoding parameters to rebuild the corrupted encoded data slice. Once the encoded data slice is rebuilt, the DST execution unit functions as previously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing section 82 of a DST client module coupled to DST execution units of a distributed storage and task network (DSTN) module via a network 24. The inbound DST processing section 82 includes a de-grouping module 180, a DS (dispersed storage) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of outbound DST processing section or may be the same modules.

In an example of operation, the DST execution units have completed execution of corresponding partial tasks on the corresponding slice groupings to produce partial results 102. The inbound DST processing section 82 receives the partial results 102 via the distributed task control module 188. The inbound DST processing section 82 then processes the partial results 102 to produce a final result, or results 104. For example, if the task was to find a specific word or phrase within data, the partial results 102 indicate where in each of the prescribed portions of the data the corresponding DST execution units found the specific word or phrase. The distributed task control module 188 combines the individual partial results 102 for the corresponding portions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82 is retrieving stored data from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices 100 corresponding to the data retrieval requests. The de-grouping module 180 receives retrieved slices 100 and de-groups them to produce encoded data slices per data partition 122. The DS error decoding module 182 decodes, in accordance with DS error encoding parameters, the encoded data slices per data partition 122 to produce data partitions 120.

The data de-partitioning module 184 combines the data partitions 120 into the data 92. The control module 186 controls the conversion of retrieve slices 100 into the data 92 using control signals 190 to each of the modules. For instance, the control module 186 provides de-grouping information to the de-grouping module 180, provides the DS error encoding parameters to the DS error decoding module 182, and provides de-partitioning information to the data de-partitioning module 184.

FIG. 14 is a logic diagram of an example of a method that is executable by distributed storage and task (DST) client module regarding inbound DST processing. The method begins at step 194 where the DST client module receives partial results. The method continues at step 196 where the DST client module retrieves the task corresponding to the partial results. For example, the partial results include header information that identifies the requesting entity, which correlates to the requested task.

The method continues at step 198 where the DST client module determines result processing information based on the task. For example, if the task were to identify a particular word or phrase within the data, the result processing information would indicate to aggregate the partial results for the corresponding portions of the data to produce the final result. As another example, if the task were to count the occurrences of a particular word or phrase within the data, results of processing the information would indicate to add the partial results to produce the final results. The method continues at step 200 where the DST client module processes the partial results in accordance with the result processing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processing of an inbound distributed storage and task (DST) processing section of a DST client module. In general, this is an inverse process of the grouping module of the outbound DST processing section of FIG. 9 . Accordingly, for each data partition (e.g., partition #1), the de-grouping module retrieves the corresponding slice grouping from the DST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, which includes the first encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 1-DST execution unit #2 provides a second slice grouping, which includes the second encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 16-30); DST execution unit #3 provides a third slice grouping, which includes the third encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 31-45); DST execution unit #4 provides a fourth slice grouping, which includes the fourth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data); and DST execution unit #5 provides a fifth slice grouping, which includes the fifth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., received slices 100) using a de-grouping selector 180 controlled by a control signal 190 as shown in the example to produce a plurality of sets of encoded data slices (e.g., retrieved slices for a partition into sets of slices 122). Each set corresponding to a data segment of the data partition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, a de-segmenting processing module 210, and a control module 186.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186, unsecures each encoded data slice 122 based on slice de-security information received as control information 190 (e.g., the compliment of the slice security information discussed with reference to FIG. 6 ) received from the control module 186. The slice security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 122, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data 158. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 122 as the sliced encoded data 158 or is bypassed such that the retrieved encoded data slices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 into encoded data segments 156 in accordance with a pillar width of the error correction encoding parameters received as control information 190 from the control module 186. For example, if the pillar width is five, the de-slicing module 204 de-slices a set of five encoded data slices into an encoded data segment 156. The error decoding module 206 decodes the encoded data segments 156 in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments 154. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed—Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, unsecures the secured data segments 154 based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module 208 is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment 154, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 154 as the data segment 152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 and receives de-segmenting information as control information 190 from the control module 186. The de-segmenting information indicates how the de-segment processing module 210 is to de-segment the data segments 152 into a data partition 120. For example, the de-segmenting information indicates how the rows and columns of data segments are to be rearranged to yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of a dispersed error decoding module. A de-slicing module 204 receives at least a decode threshold number of encoded data slices 158 for each data segment in accordance with control information 190 and provides encoded data 156. In this example, a decode threshold is three. As such, each set of encoded data slices 158 is shown to have three encoded data slices per data segment. The de-slicing module 204 may receive three encoded data slices per data segment because an associated distributed storage and task (DST) client module requested retrieving only three encoded data slices per segment or selected three of the retrieved encoded data slices per data segment. As shown, which is based on the unity matrix encoding previously discussed with reference to FIG. 8 , an encoded data slice may be a data-based encoded data slice (e.g., DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each data segment in accordance with the error correction decoding parameters of control information 190 to produce secured segments 154. In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of an inbound distributed storage and task (DST) processing. In this example, a de-segment processing module 210 receives data segments 152 (e.g., 1-8) and rearranges the data blocks of the data segments into rows and columns in accordance with de-segmenting information of control information 190 to produce a data partition 120. Note that the number of rows is based on the decode threshold (e.g., 3 in this specific example) and the number of columns is based on the number and size of the data blocks.

The de-segmenting module 210 converts the rows and columns of data blocks into the data partition 120. Note that each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data 92 within an inbound distributed storage and task (DST) processing section. As shown, the data 92 is reconstructed from a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunk set of data) is decoded and re-grouped using a de-grouping and decoding function 212 and a de-partition function 214 from slice groupings as previously discussed. For a given data partition, the slice groupings (e.g., at least a decode threshold per data segment of encoded data slices) are received from DST execution units. From data partition to data partition, the ordering of the slice groupings received from the DST execution units may vary as discussed with reference to FIG. 10 .

FIG. 20 is a diagram of an example of a distributed storage and/or retrieval within the distributed computing system. The distributed computing system includes a plurality of distributed storage and/or task (DST) processing client modules 34 (one shown) coupled to a distributed storage and/or task processing network (DSTN) module, or multiple DSTN modules, via a network 24. The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. The DSTN module includes a plurality of DST execution units. Each DST execution unit includes a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 that it desires to store in the DSTN module. The data 92 may be a file (e.g., video, audio, text, graphics, etc.), a data object, a data block, an update to a file, an update to a data block, etc. In this instance, the outbound DST processing module 80 converts the data 92 into encoded data slices 216 as will be further described with reference to FIGS. 21-23 . The outbound DST processing module 80 sends, via the network 24, to the DST execution units for storage as further described with reference to FIG. 24 .

In an example of data retrieval, the DST client module 34 issues a retrieve request to the DST execution units for the desired data 92. The retrieve request may address each DST executions units storing encoded data slices of the desired data, address a decode threshold number of DST execution units, address a read threshold number of DST execution units, or address some other number of DST execution units. In response to the request, each addressed DST execution unit retrieves its encoded data slices 100 of the desired data and sends them to the inbound DST processing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82 receives at least a decode threshold number of encoded data slices 100, it converts the encoded data slices 100 into a data segment. The inbound DST processing section 82 aggregates the data segments to produce the retrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module coupled to a distributed storage and task network (DSTN) module (e.g., a plurality of DST execution units) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 is by-passed such that data 92 is provided directly to the DS error encoding module 112. The control module 116 coordinates the by-passing of the data partitioning module 110 by outputting a bypass 220 message to the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serial manner, a parallel manner, and/or a combination thereof. The DS error encoding module 112 DS error encodes the data in accordance with control information 160 from the control module 116 to produce encoded data slices 218. The DS error encoding includes segmenting the data 92 into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)). The control information 160 indicates which steps of the DS error encoding are active for the data 92 and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of the data segments into pillars of slices 216. The number of pillars corresponds to the pillar width of the DS error encoding parameters. In this example, the distributed task control module 118 facilitates the storage request.

FIG. 22 is a schematic block diagram of an example of a dispersed storage (DS) error encoding module 112 for the example of FIG. 21 . The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives data 92 and receives segmenting information as control information 160 from the control module 116. The segmenting information indicates how the segment processing module is to segment the data. For example, the segmenting information indicates the size of each data segment. The segment processing module 142 segments the data 92 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it compresses a data segment 152, encrypts the compressed data segment, and generates a CRC value for the encrypted data segment to produce a secure data segment. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment to produce an encoded data segment.

The slicing module 148 slices the encoded data segment in accordance with a pillar width of the error correction encoding parameters. For example, if the pillar width is five, the slicing module slices an encoded data segment into a set of five encoded data slices. As such, for a plurality of data segments, the slicing module 148 outputs a plurality of sets of encoded data slices as shown within encoding and slicing function 222 as described.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it may compress an encoded data slice, encrypt the compressed encoded data slice, and generate a CRC value for the encrypted encoded data slice to produce a secure encoded data slice tweaking. When the per slice security processing module 150 is not enabled, it passes the encoded data slices or is bypassed such that the encoded data slices 218 are the output of the DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillar slice groups utilizing encoding, slicing and pillar grouping function 224 for storage in memory of a distributed storage and task network (DSTN) module. As previously discussed the data 92 is encoded and sliced into a plurality of sets of encoded data slices; one set per data segment. The grouping selection module organizes the sets of encoded data slices into pillars of data slices. In this example, the DS error encoding parameters include a pillar width of 5 and a decode threshold of 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selection module takes the first encoded data slice of each of the sets and forms a first pillar, which may be sent to the first DST execution unit. Similarly, the grouping selection module creates the second pillar from the second slices of the sets; the third pillar from the third slices of the sets; the fourth pillar from the fourth slices of the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributed storage and/or task (DST) execution unit that includes an interface 169, a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34. A computing core 26 may be utilized to implement the one or more DT execution modules 90 and the DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a pillar of slices 216, the DST execution unit receives, via interface 169, a pillar of slices 216 (e.g., pillar #1 slices). The memory 88 stores the encoded data slices 216 of the pillar of slices in accordance with memory control information 174 it receives from the controller 86. The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on distributed storage information (e.g., user information (e.g., user ID, distributed storage permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, etc.), etc.). Similarly, when retrieving slices, the DST execution unit receives, via interface 169, a slice retrieval request. The memory 88 retrieves the slice in accordance with memory control information 174 it receives from the controller 86. The memory 88 outputs the slice 100, via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of an inbound distributed storage and/or task (DST) processing section 82 for retrieving dispersed error encoded data 92. The inbound DST processing section 82 includes a de-grouping module 180, a dispersed storage (DS) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of an outbound DST processing section or may be the same modules.

In an example of operation, the inbound DST processing section 82 is retrieving stored data 92 from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices corresponding to data retrieval requests from the distributed task control module 188. The de-grouping module 180 receives pillars of slices 100 and de-groups them in accordance with control information 190 from the control module 186 to produce sets of encoded data slices 218. The DS error decoding module 182 decodes, in accordance with the DS error encoding parameters received as control information 190 from the control module 186, each set of encoded data slices 218 to produce data segments, which are aggregated into retrieved data 92. The data de-partitioning module 184 is by-passed in this operational mode via a bypass signal 226 of control information 190 from the control module 186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, and a de-segmenting processing module 210. The dispersed error decoding module 182 is operable to de-slice and decode encoded slices per data segment 218 utilizing a de-slicing and decoding function 228 to produce a plurality of data segments that are de-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186 via control information 190, unsecures each encoded data slice 218 based on slice de-security information (e.g., the compliment of the slice security information discussed with reference to FIG. 6 ) received as control information 190 from the control module 186. The slice de-security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 218, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 218 as the sliced encoded data or is bypassed such that the retrieved encoded data slices 218 are provided as the sliced encoded data.

The de-slicing module 204 de-slices the sliced encoded data into encoded data segments in accordance with a pillar width of the error correction encoding parameters received as control information 190 from a control module 186. For example, if the pillar width is five, the de-slicing module de-slices a set of five encoded data slices into an encoded data segment. Alternatively, the encoded data segment may include just three encoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, unsecures the secured data segments based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 152 as the data segment or is bypassed. The de-segmenting processing module 210 aggregates the data segments 152 into the data 92 in accordance with control information 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module that includes a plurality of distributed storage and task (DST) execution units (#1 through #n, where, for example, n is an integer greater than or equal to three). Each of the DST execution units includes a DST client module 34, a controller 86, one or more DT (distributed task) execution modules and memory 88.

In this example, the DSTN module stores, in the memory of the DST execution units, a plurality of DS (dispersed storage) encoded data (e.g., 1 through n, where n is an integer greater than or equal to two) and stores a plurality of DS encoded task codes (e.g., 1 through k, where k is an integer greater than or equal to two). The DS encoded data may be encoded in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups). The data that is encoded into the DS encoded data may be of any size and/or of any content. For example, the data may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc. The tasks may be encoded into the DS encoded task code in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of a DST processing unit issues a DST request to the DSTN module. The DST request may include a request to retrieve stored data, or a portion thereof, may include a request to store data that is included with the DST request, may include a request to perform one or more tasks on stored data, may include a request to perform one or more tasks on data included with the DST request, etc. In the cases where the DST request includes a request to store data or to retrieve data, the client module and/or the DSTN module processes the request as previously discussed with reference to one or more of FIGS. 3-19 (e.g., slice groupings) and/or 20-26 (e.g., pillar groupings). In the case where the DST request includes a request to perform one or more tasks on data included with the DST request, the DST client module and/or the DSTN module process the DST request as previously discussed with reference to one or more of FIGS. 3-19 .

In the case where the DST request includes a request to perform one or more tasks on stored data, the DST client module and/or the DSTN module processes the DST request as will be described with reference to one or more of FIGS. 28-39 . In general, the DST client module identifies data and one or more tasks for the DSTN module to execute upon the identified data. The DST request may be for a one-time execution of the task or for an on-going execution of the task. As an example of the latter, as a company generates daily emails, the DST request may be to daily search new emails for inappropriate content and, if found, record the content, the email sender(s), the email recipient(s), email routing information, notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributed computing system performing tasks on stored data. In this example, two distributed storage and task (DST) client modules 1-2 are shown: the first may be associated with a user device and the second may be associated with a DST processing unit or a high priority user device (e.g., high priority clearance user, system administrator, etc.). Each DST client module includes a list of stored data 234 and a list of tasks codes 236. The list of stored data 234 includes one or more entries of data identifying information, where each entry identifies data stored in the DSTN module 22. The data identifying information (e.g., data ID) includes one or more of a data file name, a data file directory listing, DSTN addressing information ofthe data, a data object identifier, etc. The list of tasks 236 includes one or more entries of task code identifying information, when each entry identifies task codes stored in the DSTN module 22. The task code identifying information (e.g., task ID) includes one or more of a task file name, a task file directory listing, DSTN addressing information of the task, another type of identifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are each smaller in number of entries for the first DST client module than the corresponding lists of the second DST client module. This may occur because the user device associated with the first DST client module has fewer privileges in the distributed computing system than the device associated with the second DST client module. Alternatively, this may occur because the user device associated with the first DST client module serves fewer users than the device associated with the second DST client module and is restricted by the distributed computing system accordingly. As yet another alternative, this may occur through no restraints by the distributed computing system, it just occurred because the operator of the user device associated with the first DST client module has selected fewer data and/or fewer tasks than the operator of the device associated with the second DST client module.

In an example of operation, the first DST client module selects one or more data entries 238 and one or more tasks 240 from its respective lists (e.g., selected data ID and selected task ID). The first DST client module sends its selections to a task distribution module 232. The task distribution module 232 may be within a stand-alone device of the distributed computing system, may be within the user device that contains the first DST client module, or may be within the DSTN module 22.

Regardless of the task distribution module's location, it generates DST allocation information 242 from the selected task ID 240 and the selected data ID 238. The DST allocation information 242 includes data partitioning information, task execution information, and/or intermediate result information. The task distribution module 232 sends the DST allocation information 242 to the DSTN module 22. Note that one or more examples of the DST allocation information will be discussed with reference to one or more of FIGS. 29-39 .

The DSTN module 22 interprets the DST allocation information 242 to identify the stored DS encoded data (e.g., DS error encoded data 2) and to identify the stored DS error encoded task code (e.g., DS error encoded task code 1). In addition, the DSTN module 22 interprets the DST allocation information 242 to determine how the data is to be partitioned and how the task is to be partitioned. The DSTN module 22 also determines whether the selected DS error encoded data 238 needs to be converted from pillar grouping to slice grouping. If so, the DSTN module 22 converts the selected DS error encoded data into slice groupings and stores the slice grouping DS error encoded data by overwriting the pillar grouping DS error encoded data or by storing it in a different location in the memory of the DSTN module 22 (i.e., does not overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in the DST allocation information 242 and sends the portions to selected DST execution units of the DSTN module 22. Each of the selected DST execution units performs its partial task(s) on its slice groupings to produce partial results. The DSTN module 22 collects the partial results from the selected DST execution units and provides them, as result information 244, to the task distribution module. The result information 244 may be the collected partial results, one or more final results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242, or one or more intermediate results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242.

The task distribution module 232 receives the result information 244 and provides one or more final results 104 therefrom to the first DST client module. The final result(s) 104 may be result information 244 or a result(s) of the task distribution module's processing of the result information 244.

In concurrence with processing the selected task of the first DST client module, the distributed computing system may process the selected task(s) of the second DST client module on the selected data(s) of the second DST client module. Alternatively, the distributed computing system may process the second DST client module's request subsequent to, or preceding, that of the first DST client module. Regardless of the ordering and/or parallel processing of the DST client module requests, the second DST client module provides its selected data 238 and selected task 240 to a task distribution module 232. If the task distribution module 232 is a separate device of the distributed computing system or within the DSTN module, the task distribution modules 232 coupled to the first and second DST client modules may be the same module. The task distribution module 232 processes the request of the second DST client module in a similar manner as it processed the request of the first DST client module.

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module 232 facilitating the example of FIG. 28 . The task distribution module 232 includes a plurality of tables it uses to generate distributed storage and task (DST) allocation information 242 for selected data and selected tasks received from a DST client module. The tables include data storage information 248, task storage information 250, distributed task (DT) execution module information 252, and task↔sub-task mapping information 246.

The data storage information table 248 includes a data identification (ID) field 260, a data size field 262, an addressing information field 264, distributed storage (DS) information 266, and may further include other information regarding the data, how it is stored, and/or how it can be processed. For example, DS encoded data #1 has a data ID of 1, a data size of AA (e.g., a byte size of a few Terabytes or more), addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1; and SLC_1. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the data and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the data, physical addresses of the first storage word or the storage words of the data, may be a list of slice names of the encoded data slices of the data, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEGA, per slice security information (e.g., SLC_1), and/or any other information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification (ID) field 268, a task size field 270, an addressing information field 272, distributed storage (DS) information 274, and may further include other information regarding the task, how it is stored, and/or how it can be used to process data. For example, DS encoded task #2 has a task ID of 2, a task size of XY, addressing information of Addr2XY, and DS parameters of 3/5; SEG_2; and SLC_2. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the task and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the task, physical addresses of the first storage word or the storage words of the task, may be a list of slices names of the encoded slices of the task code, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_2), per slice security information (e.g., SLC_2), and/or any other information regarding how the task was encoded into encoded task slices. Note that the segment and/or the per-slice security information include a type of encryption (if enabled), a type of compression (if enabled), watermarking information (if enabled), and/or an integrity check scheme (if enabled).

The task↔sub-task mapping information table 246 includes a task field 256 and a sub-task field 258. The task field 256 identifies a task stored in the memory of a distributed storage and task network (DSTN) module and the corresponding sub-task fields 258 indicates whether the task includes sub-tasks and, if so, how many and if any of the sub-tasks are ordered. In this example, the task↔sub-task mapping information table 246 includes an entry for each task stored in memory of the DSTN module (e.g., task 1 through task k). In particular, this example indicates that task 1 includes 7 sub-tasks; task 2 does not include sub-tasks, and task k includes r number of sub-tasks (where r is an integer greater than or equal to two). The DT execution module table 252 includes a DST execution unit ID field 276, a DT execution module ID field 278, and a DT execution module capabilities field 280. The DST execution unit ID field 276 includes the identity of DST units in the DSTN module. The DT execution module ID field 278 includes the identity of each DT execution unit in each DST unit. For example, DST unit 1 includes three DT executions modules (e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280 includes identity of the capabilities of the corresponding DT execution unit. For example, DT execution module 1_1 includes capabilities X, where X includes one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.), and/or any information germane to executing one or more tasks.

From these tables, the task distribution module 232 generates the DST allocation information 242 to indicate where the data is stored, how to partition the data, where the task is stored, how to partition the task, which DT execution units should perform which partial task on which data partitions, where and how intermediate results are to be stored, etc. If multiple tasks are being performed on the same data or different data, the task distribution module factors such information into its generation of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computing system performing tasks on stored data as a task flow 318. In this example, selected data 92 is data 2 and selected tasks are tasks 1, 2, and 3. Task 1 corresponds to analyzing translation of data from one language to another (e.g., human language or computer language); task 2 corresponds to finding specific words and/or phrases in the data; and task 3 corresponds to finding specific translated words and/or phrases in translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identify non-words (non-ordered); task 1_2—identify unique words (non-ordered); task 1_3—translate (non-ordered); task 1_4—translate back (ordered after task 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task 1_6—determine non-word translation errors (ordered after task 1_5 and 1_1); and task 1_7—determine correct translations (ordered after 1_5 and 1_2). The sub-task further indicates whether they are an ordered task (i.e., are dependent on the outcome of another task) or non-order (i.e., are independent of the outcome of another task). Task 2 does not include sub-tasks and task 3 includes two sub-tasks: task 3_1 translate; and task 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze data for translation accuracies, translation errors, translation anomalies, occurrence of specific words or phrases in the data, and occurrence of specific words or phrases on the translated data. Graphically, the data 92 is translated 306 into translated data 282; is analyzed for specific words and/or phrases 300 to produce a list of specific words and/or phrases 286; is analyzed for non-words 302 (e.g., not in a reference dictionary) to produce a list of non-words 290; and is analyzed for unique words 316 included in the data 92 (i.e., how many different words are included in the data) to produce a list of unique words 298. Each of these tasks is independent of each other and can therefore be processed in parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specific translated words and/or phrases 304 to produce a list of specific translated words and/or phrases 288. The translated data 282 is translated back 308 (e.g., sub-task 1_4) into the language of the original data to produce re-translated data 284. These two tasks are dependent on the translate task (e.g., task 1_3) and thus must be ordered after the translation task, which may be in a pipelined ordering or a serial ordering. The re-translated data 284 is then compared 310 with the original data 92 to find words and/or phrases that did not translate (one way and/or the other) properly to produce a list of incorrectly translated words 294. As such, the comparing task (e.g., sub-task 1_5) 310 is ordered after the translation 306 and re-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the list of non-words 290 to identify words that were not properly translated because the words are non-words to produce a list of errors due to non-words 292. In addition, the list of words incorrectly translated 294 is compared 314 to the list of unique words 298 to identify unique words that were properly translated to produce a list of correctly translated words 296. The comparison may also identify unique words that were not properly translated to produce a list of unique words that were not properly translated. Note that each list of words (e.g., specific words and/or phrases, non-words, unique words, translated words and/or phrases, etc.,) may include the word and/or phrase, how many times it is used, where in the data it is used, and/or any other information requested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30 . As shown, DS encoded data 2 is stored as encoded data slices across the memory (e.g., stored in memories 88) of DST execution units 1-5; the DS encoded task code 1 (of task 1) and DS encoded task 3 are stored as encoded task slices across the memory of DST execution units 1-5; and DS encoded task code 2 (of task 2) is stored as encoded task slices across the memory of DST execution units 3-7. As indicated in the data storage information table and the task storage information table of FIG. 29 , the respective data/task has DS parameters of 3/5 for their decode threshold/pillar width; hence spanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST) allocation information 242 for the example of FIG. 30 . The DST allocation information 242 includes data partitioning information 320, task execution information 322, and intermediate result information 324. The data partitioning information 320 includes the data identifier (ID), the number of partitions to split the data into, address information for each data partition, and whether the DS encoded data has to be transformed from pillar grouping to slice grouping. The task execution information 322 includes tabular information having a task identification field 326, a task ordering field 328, a data partition field ID 330, and a set of DT execution modules 332 to use for the distributed task processing per data partition. The intermediate result information 324 includes tabular information having a name ID field 334, an ID of the DST execution unit assigned to process the corresponding intermediate result 336, a scratch pad storage field 338, and an intermediate result storage field 340.

Continuing with the example of FIG. 30 , where tasks 1-3 are to be distributedly performed on data 2, the data partitioning information includes the ID of data 2. In addition, the task distribution module determines whether the DS encoded data 2 is in the proper format for distributed computing (e.g., was stored as slice groupings). If not, the task distribution module indicates that the DS encoded data 2 format needs to be changed from the pillar grouping format to the slice grouping format, which will be done the by DSTN module. In addition, the task distribution module determines the number of partitions to divide the data into (e.g., 2_1 through 2_z) and addressing information for each partition.

The task distribution module generates an entry in the task execution information section for each sub-task to be performed. For example, task 1_1 (e.g., identify non-words on the data) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 search for non-words in data partitions 2_1 through 2_z to produce task 1_1 intermediate results (R1-1, which is a list of non-words). Task 1_2 (e.g., identify unique words) has similar task execution information as task 1_1 to produce task 1_2 intermediate results (R1-2, which is the list of unique words).

Task 1_3 (e.g., translate) includes task execution information as being non-ordered (i.e., is independent), having DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and having DT execution modules 12, 22, 3_2, 4_2, and 52 translate data partitions 2_5 through 2_z to produce task 1_3 intermediate results (R1-3, which is the translated data). In this example, the data partitions are grouped, where different sets of DT execution modules perform a distributed sub-task (or task) on each data partition group, which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to be executed on task 1_3's intermediate result (e.g., R1-3_1) (e.g., the translated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and are allocated to translate back task 1_3 intermediate result partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back task 1_3 intermediate result partitions R1-3_5 through R1-3_z to produce task 1-4 intermediate results (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translation errors) is ordered after task 1_4 and is to be executed on task 1_4's intermediate results (R4-1) and on the data. DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1 through 2_z) with partitions of task 1-4 intermediate results partitions R1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5, which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered after tasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1 intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_6 intermediate results (R1-6, which is the list translation errors due to non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered after tasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2, 3_2, 4_2, and 52 are allocated to compare the partitions of task 1_2 intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_7 intermediate results (R1-7, which is the list of correctly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 31, 41, Si, 6_1, and 71. For instance, DT execution modules 31, 41, Si, 6_1, and 71 search for specific words and/or phrases in data partitions 2_1 through 2_z to produce task 2 intermediate results (R2, which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) is ordered after task 1_3 (e.g., translate) is to be performed on partitions R1-3_1 through R1-3_z by DT execution modules 1_2, 22, 3_2, 42, and 52. For instance, DT execution modules 1_2, 22, 3_2, 4_2, and 52 search for specific translated words and/or phrases in the partitions of the translated data (R1-3_1 through R1-3_z) to produce task 3 2 intermediate results (R3-2, which is a list of specific translated words and/or phrases).

For each task, the intermediate result information indicates which DST unit is responsible for overseeing execution of the task and, if needed, processing the partial results generated by the set of allocated DT execution units. In addition, the intermediate result information indicates a scratch pad memory for the task and where the corresponding intermediate results are to be stored. For example, for intermediate result R1-1 (the intermediate result of task 1_1), DST unit 1 is responsible for overseeing execution of the task 1_1 and coordinates storage of the intermediate result as encoded intermediate result slices stored in memory of DST execution units 1-5. In general, the scratch pad is for storing non-DS encoded intermediate results and the intermediate result storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage and task network (DSTN) module performing the example of FIG. 30 . In FIG. 33 , the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with distributed storage and task network (DST) allocation information. For each data partition, the DSTN identifies a set of its DT (distributed task) execution modules 90 to perform the task (e.g., identify non-words (i.e., not in a reference dictionary) within the data partition) in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules 90 may be the same, different, or a combination thereof (e.g., some data partitions use the same set while other data partitions use different sets).

For the first data partition, the first set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32 ) executes task 1_1 to produce a first partial result 102 of non-words found in the first data partition. The second set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32 ) executes task 1_1 to produce a second partial result 102 of non-words found in the second data partition. The sets of DT execution modules (as per the DST allocation information) perform task 1_1 on the data partitions until the “z” set of DT execution modules performs task 1_1 on the “zth” data partition to produce a “zth” partial result 102 of non-words found in the “zth” data partition.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 1 is assigned to process the first through “zth” partial results to produce the first intermediate result (R1-1), which is a list of non-words found in the data. For instance, each set of DT execution modules 90 stores its respective partial result in the scratchpad memory of DST execution unit 1 (which is identified in the DST allocation or may be determined by DST execution unit 1). A processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results to produce the first intermediate result (e.g., R1_1). The processing module stores the first intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the first intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of non-words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the first intermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1 through R1-1_m). If the first intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the first intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 34 , the DSTN module is performing task 1_2 (e.g., find unique words) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 1_2 to produce a partial results (e.g., 1^(st) through “zth”) of unique words found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 1 is assigned to process the first through “zth” partial results 102 of task 1_2 to produce the second intermediate result (R1-2), which is a list of unique words found in the data 92. The processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of unique words to produce the second intermediate result. The processing module stores the second intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the second intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of unique words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the second intermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1 through R1-2_m). If the second intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the second intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 35 , the DSTN module is performing task 1_3 (e.g., translate) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_3 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and DT execution modules 12, 2_2, 3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z). For the data partitions, the allocated set of DT execution modules 90 executes task 1_3 to produce partial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_3 to produce the third intermediate result (R1-3), which is translated data. The processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of translated data to produce the third intermediate result. The processing module stores the third intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the third intermediate result (e.g., translated data). To begin the encoding, the DST client module partitions the third intermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1 through R1-3_y). For each partition of the third intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is further shown in FIG. 35 , the DSTN module is performing task 1_4 (e.g., retranslate) on the translated data of the third intermediate result. To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition of the third intermediate result, the DSTN identifies a set of its DT execution modules 90 to perform task 1_4 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to translate back partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitions R1-3_5 through R1-3_z). For the partitions, the allocated set of DT execution modules executes task 1_4 to produce partial results 102 (e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_4 to produce the fourth intermediate result (R1-4), which is retranslated data. The processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of retranslated data to produce the fourth intermediate result. The processing module stores the fourth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the fourth intermediate result (e.g., retranslated data). To begin the encoding, the DST client module partitions the fourth intermediate result (R1-4) into a plurality of partitions (e.g., R1-4_1 through R1-4_z). For each partition of the fourth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 36 , a distributed storage and task network (DSTN) module is performing task 1_5 (e.g., compare) on data 92 and retranslated data of FIG. 35 . To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. The DSTN module also accesses the retranslated data from the scratchpad memory, or from the intermediate result memory and decodes it, and partitions it into a plurality of partitions in accordance with the DST allocation information. The number of partitions of the retranslated data corresponds to the number of partitions of the data.

For each pair of partitions (e.g., data partition 1 and retranslated data partition 1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_5 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 1 is assigned to process the first through “zth” partial results of task 1_5 to produce the fifth intermediate result (R1-5), which is the list of incorrectly translated words and/or phrases. In particular, the processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases to produce the fifth intermediate result. The processing module stores the fifth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the fifth intermediate result. To begin the encoding, the DST client module partitions the fifth intermediate result (R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). For each partition of the fifth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36 , the DSTN module is performing task 1_6 (e.g., translation errors due to non-words) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of non-words (e.g., the first intermediate result R1-1). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_6 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_6 to produce the sixth intermediate result (R1-6), which is the list of incorrectly translated words and/or phrases due to non-words. In particular, the processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases due to non-words to produce the sixth intermediate result. The processing module stores the sixth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the sixth intermediate result. To begin the encoding, the DST client module partitions the sixth intermediate result (R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). For each partition of the sixth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36 , the DSTN module is performing task 1_7 (e.g., correctly translated words and/or phrases) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of unique words (e.g., the second intermediate result R1-2). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions. For each pair of partitions (e.g., partition R1-2_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_7 in accordance with the DST allocation information (e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pair of partitions, the allocated set of DT execution modules executes task 1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_7 to produce the seventh intermediate result (R1-7), which is the list of correctly translated words and/or phrases. In particular, the processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of the list of correctly translated words and/or phrases to produce the seventh intermediate result. The processing module stores the seventh intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the seventh intermediate result. To begin the encoding, the DST client module partitions the seventh intermediate result (R1-7) into a plurality of partitions (e.g., R1-7_1 through R1-7_z). For each partition of the seventh intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 37 , the distributed storage and task network (DSTN) module is performing task 2 (e.g., find specific words and/or phrases) on the data 92. To begin, the DSTN module accesses the data and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules 90 to perform task 2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 2 to produce partial results 102 (e.g., 1^(st) through “zth”) of specific words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 7 is assigned to process the first through “zth” partial results of task 2 to produce task 2 intermediate result (R2), which is a list of specific words and/or phrases found in the data. The processing module of DST execution 7 is engaged to aggregate the first through “zth” partial results of specific words and/or phrases to produce the task 2 intermediate result. The processing module stores the task 2 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice grouping based DS error encode the task 2 intermediate result. To begin the encoding, the DST client module determines whether the list of specific words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 2 intermediate result (R2) into a plurality of partitions (e.g., R2_1 through R2_m). If the task 2 intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the task 2 intermediate result, or for the task 2 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38 , the distributed storage and task network (DSTN) module is performing task 3 (e.g., find specific translated words and/or phrases) on the translated data (R1-3). To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition, the DSTN identifies a set of its DT execution modules to perform task 3 in accordance with the DST allocation information. From partition to partition, the set of DT execution modules may be the same, different, or a combination thereof. For the partitions, the allocated set of DT execution modules executes task 3 to produce partial results 102 (e.g., 1^(st) through “zth”) of specific translated words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32 , DST execution unit 5 is assigned to process the first through “zth” partial results of task 3 to produce task 3 intermediate result (R3), which is a list of specific translated words and/or phrases found in the translated data. In particular, the processing module of DST execution 5 is engaged to aggregate the first through “zth” partial results of specific translated words and/or phrases to produce the task 3 intermediate result. The processing module stores the task 3 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slice grouping based DS error encode the task 3 intermediate result. To begin the encoding, the DST client module determines whether the list of specific translated words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 3 intermediate result (R3) into a plurality of partitions (e.g., R3_1 through R3_m). If the task 3 intermediate result is not of sufficient size to partition, it is not partitioned. For each partition of the task 3 intermediate result, or for the task 3 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information into final results 104 for the example of FIG. 30 . In this example, the result information includes the list of specific words and/or phrases found in the data (task 2 intermediate result), the list of specific translated words and/or phrases found in the data (task 3 intermediate result), the list of non-words found in the data (task 1 first intermediate result R1-1), the list of unique words found in the data (task 1 second intermediate result R1-2), the list of translation errors due to non-words (task 1 sixth intermediate result R1-6), and the list of correctly translated words and/or phrases (task 1 seventh intermediate result R1-7). The task distribution module provides the result information to the requesting DST client module as the results 104.

FIG. 40A is a schematic block diagram of an embodiment of a decentralized agreement module 350 that includes a set of deterministic functions 1-N, a set of normalizing functions 1-N, a set of scoring functions 1-N, and a ranking function 352. Each of the deterministic function, the normalizing function, the scoring function, and the ranking function 352, may be implemented utilizing the processing module 84 of FIG. 3 . The decentralized agreement module 350 may be implemented utilizing any module and/or unit of a dispersed storage network (DSN). For example, the decentralized agreement module is implemented utilizing the distributed storage and task (DST) client module 34 of FIG. 1 .

The decentralized agreement module 350 functions to receive a ranked scoring information request 354 and to generate ranked scoring information 358 based on the ranked scoring information request 354 and other information. The ranked scoring information request 354 includes one or more of an asset identifier (ID) 356 of an asset associated with the request, an asset type indicator, one or more location identifiers of locations associated with the DSN, one or more corresponding location weights, and a requesting entity ID. The asset includes any portion of data associated with the DSN including one or more asset types including a data object, a data record, an encoded data slice, a data segment, a set of encoded data slices, and a plurality of sets of encoded data slices. As such, the asset ID 356 of the asset includes one or more of a data name, a data record identifier, a source name, a slice name, and a plurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examples of locations includes one or more of a storage unit, a memory device of the storage unit, a site, a storage pool of storage units, a pillar index associated with each encoded data slice of a set of encoded data slices generated by an information dispersal algorithm (IDA), a DST client module 34 of FIG. 1 , a DST processing unit 16 of FIG. 1 , a DST integrity processing unit 20 of FIG. 1 , a DSTN managing unit 18 of FIG. 1 , a user device 12 of FIG. 1 , and a user device 14 of FIG. 1 .

Each location is associated with a location weight based on one or more of a resource prioritization of utilization scheme and physical configuration of the DSN. The location weight includes an arbitrary bias which adjusts a proportion of selections to an associated location such that a probability that an asset will be mapped to that location is equal to the location weight divided by a sum of all location weights for all locations of comparison. For example, each storage pool of a plurality of storage pools is associated with a location weight based on storage capacity. For instance, storage pools with more storage capacity are associated with higher location weights than others. The other information may include a set of location identifiers and a set of location weights associated with the set of location identifiers. For example, the other information includes location identifiers and location weights associated with a set of memory devices of a storage unit when the requesting entity utilizes the decentralized agreement module 350 to produce ranked scoring information 358 with regards to selection of a memory device of the set of memory devices for accessing a particular encoded data slice (e.g., where the asset ID includes a slice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identical ranked scoring information for each ranked scoring information request that includes substantially identical content of the ranked scoring information request. For example, a first requesting entity issues a first ranked scoring information request to the decentralized agreement module 350 and receives first ranked scoring information. A second requesting entity issues a second ranked scoring information request to the decentralized agreement module and receives second ranked scoring information. The second ranked scoring information is substantially the same as the first ranked scoring information when the second ranked scoring information request is substantially the same as the first ranked scoring information request.

As such, two or more requesting entities may utilize the decentralized agreement module 350 to determine substantially identical ranked scoring information. As a specific example, the first requesting entity selects a first storage pool of a plurality of storage pools for storing a set of encoded data slices utilizing the decentralized agreement module 350 and the second requesting entity identifies the first storage pool of the plurality of storage pools for retrieving the set of encoded data slices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350 receives the ranked scoring information request 354. Each deterministic function performs a deterministic function on a combination and/or concatenation (e.g., add, append, interleave) of the asset ID 356 of the ranked scoring information request 354 and an associated location ID of the set of location IDs to produce an interim result. The deterministic function includes at least one of a hashing function, a hash-based message authentication code function, a mask generating function, a cyclic redundancy code function, hashing module of a number of locations, consistent hashing, rendezvous hashing, and a sponge function. As a specific example, deterministic function 2 appends a location ID 2 of a storage pool 2 to a source name as the asset ID to produce a combined value and performs the mask generating function on the combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs a normalizing function on a corresponding interim result to produce a corresponding normalized interim result. The performing of the normalizing function includes dividing the interim result by a number of possible permutations of the output of the deterministic function to produce the normalized interim result. For example, normalizing function 2 performs the normalizing function on the interim result 2 to produce a normalized interim result 2.

With a set of normalized interim results 1-N, each scoring function performs a scoring function on a corresponding normalized interim result to produce a corresponding score. The performing of the scoring function includes dividing an associated location weight by a negative log of the normalized interim result. For example, scoring function 2 divides location weight 2 of the storage pool 2 (e.g., associated with location ID 2) by a negative log of the normalized interim result 2 to produce a score 2.

With a set of scores 1-N, the ranking function 352 performs a ranking function on the set of scores 1-N to generate the ranked scoring information 358. The ranking function includes rank ordering each score with other scores of the set of scores 1-N, where a highest score is ranked first. As such, a location associated with the highest score may be considered a highest priority location for resource utilization (e.g., accessing, storing, retrieving, etc., the given asset of the request). Having generated the ranked scoring information 358, the decentralized agreement module 350 outputs the ranked scoring information 358 to the requesting entity.

FIG. 40B is a flowchart illustrating an example of selecting a resource. The method begins or continues at step 360 where a processing module (e.g., of a decentralized agreement module) receives a ranked scoring information request from a requesting entity with regards to a set of candidate resources. For each candidate resource, the method continues at step 362 where the processing module performs a deterministic function on a location identifier (ID) of the candidate resource and an asset ID of the ranked scoring information request to produce an interim result. As a specific example, the processing module combines the asset ID and the location ID of the candidate resource to produce a combined value and performs a hashing function on the combined value to produce the interim result.

For each interim result, the method continues at step 364 where the processing module performs a normalizing function on the interim result to produce a normalized interim result. As a specific example, the processing module obtains a permutation value associated with the deterministic function (e.g., maximum number of permutations of output of the deterministic function) and divides the interim result by the permutation value to produce the normalized interim result (e.g., with a value between 0 and 1).

For each normalized interim result, the method continues at step 366 where the processing module performs a scoring function on the normalized interim result utilizing a location weight associated with the candidate resource associated with the interim result to produce a score of a set of scores. As a specific example, the processing module divides the location weight by a negative log of the normalized interim result to produce the score.

The method continues at step 368 where the processing module rank orders the set of scores to produce ranked scoring information (e.g., ranking a highest value first). The method continues at step 370 where the processing module outputs the ranked scoring information to the requesting entity. The requesting entity may utilize the ranked scoring information to select one location of a plurality of locations. FIG. 40C is a schematic block diagram of an embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and the distributed storage and task network (DSTN) module 22 of FIG. 1 . Hereafter, the DSTN module 22 may be interchangeably referred to as a DSN memory. The DST processing unit 16 includes a decentralized agreement module 380 and the DST client module 34 of FIG. 1 . The decentralized agreement module 380 be implemented utilizing the decentralized agreement module 350 of FIG. 40A. The DSTN module 22 includes a plurality of DST execution (EX) unit pools 1-P. Each DST execution unit pool includes one or more sites 1-S. Each site includes one or more DST execution units 1-N. Each DST execution unit may be associated with at least one pillar of N pillars associated with an information dispersal algorithm (IDA), where a data segment is dispersed storage error encoded using the IDA to produce one or more sets of encoded data slices, and where each set includes N encoded data slices and like encoded data slices (e.g., slice 3's) of two or more sets of encoded data slices are included in a common pillar (e.g., pillar 3). Each site may not include every pillar and a given pillar may be implemented at more than one site. Each DST execution unit includes a plurality of memories 1-M. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 . Hereafter, a DST execution unit may be referred to interchangeably as a storage unit and a set of DST execution units may be interchangeably referred to as a set of storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resources of at least one DST execution unit pool for data access, utilize the selected DST execution unit pool for the data access, and issue a data access response 392 based on the data access. The selecting of the resources includes utilizing a decentralized agreement function of the decentralized agreement module 380, where a plurality of locations are ranked against each other. The selecting may include selecting one storage pool of the plurality of storage pools, selecting DST execution units at various sites of the plurality of sites, selecting a memory of the plurality of memories for each DST execution unit, and selecting combinations of memories, DST execution units, sites, pillars, and storage pools.

In an example of operation, the DST client module 34 receives the data access request 382 from a requesting entity, where the data access request 382 includes at least one of a store data request, a retrieve data request, a delete data request, a data name, and a requesting entity identifier (ID). Having received the data access request 382, the DST client module 34 determines a DSN address associated with the data access request. The DSN address includes at least one of a source name (e.g., including a vault ID and an object number associated with the data name), a data segment ID, a set of slice names, a plurality of sets of slice names. The determining includes at least one of generating (e.g., for the store data request) and retrieving (e.g., from a DSN directory, from a dispersed hierarchical index) based on the data name (e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects a plurality of resource levels (e.g., DST EX unit pool, site, DST execution unit, pillar, memory) associated with the DSTN module 22. The determining may be based on one or more of the data name, the requesting entity ID, a predetermination, a lookup, a DSN performance indicator, and interpreting an error message. For example, the DST client module 34 selects the DST execution unit pool as a first resource level and a set of memory devices of a plurality of memory devices as a second resource level based on a system registry lookup for a vault associated with the requesting entity.

Having selected the plurality resource levels, the DST client module 34, for each resource level, issues a ranked scoring information request 384 to the decentralized agreement module 380 utilizing the DSN address as an asset ID. The decentralized agreement module 380 performs the decentralized agreement function based on the asset ID (e.g., the DSN address), identifiers of locations of the selected resource levels, and location weights of the locations to generate ranked scoring information 386.

For each resource level, the DST client module 34 receives corresponding ranked scoring information 386. Having received the ranked scoring information 386, the DST client module 34 identifies one or more resources associated with the resource level based on the ranked scoring information 386. For example, the DST client module 34 identifies a DST execution unit pool associated with a highest score and identifies a set of memory devices within DST execution units of the identified DST execution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34 accesses the DSTN module 22 based on the identified one or more resources associated with each resource level. For example, the DST client module 34 issues resource access requests 388 (e.g., write slice requests when storing data, read slice requests when recovering data) to the identified DST execution unit pool, where the resource access requests 388 further identify the identified set of memory devices. Having accessed the DSTN module 22, the DST client module 34 receives resource access responses 390 (e.g., write slice responses, read slice responses). The DST client module 34 issues the data access response 392 based on the received resource access responses 390. For example, the DST client module 34 decodes received encoded data slices to reproduce data and generates the data access response 392 to include the reproduced data.

FIG. 40D is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory. The method begins or continues at step 394 where a processing module (e.g., of a distributed storage and task (DST) client module) receives a data access request from a requesting entity. The data access request includes one or more of a storage request, a retrieval request, a requesting entity identifier, and a data identifier (ID). The method continues at step 396 where the processing module determines a DSN address associated with the data access request. For example, the processing module generates the DSN address for the storage request. As another example, the processing module performs a lookup for the retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects a plurality of resource levels associated with the DSN memory. The selecting may be based on one or more of a predetermination, a range of weights associated with available resources, a resource performance level, and a resource performance requirement level. For each resource level, the method continues at step 400 where the processing module determines ranked scoring information. For example, the processing module issues a ranked scoring information request to a decentralized agreement module based on the DSN address and receives corresponding ranked scoring information for the resource level, where the decentralized agreement module performs a decentralized agreement protocol function on the DSN address using the associated resource identifiers and resource weights for the resource level to produce the ranked scoring information for the resource level.

For each resource level, the method continues at step 402 where the processing module selects one or more resources associated with the resource level based on the ranked scoring information. For example, the processing module selects a resource associated with a highest score when one resource is required. As another example, the processing module selects a plurality of resources associated with highest scores when a plurality of resources are required.

The method continues at step 404 where the processing module accesses the DSN memory utilizing the selected one or more resources for each of the plurality of resource levels. For example, the processing module identifies network addressing information based on the selected resources including one or more of a storage unit Internet protocol address and a memory device identifier, generates a set of encoded data slice access requests based on the data access request and the DSN address, and sends the set of encoded data slice access requests to the DSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues a data access response to the requesting entity based on one or more resource access responses from the DSN memory. For example, the processing module issues a data storage status indicator when storing data. As another example, the processing module generates the data access response to include recovered data when retrieving data.

FIGS. 41A and 41C are a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes a plurality of access units 1-N, the network 24 of FIG. 1 , and a distributed storage and task (DST) execution (EX) unit set 410. Each access unit includes a decentralized agreement module 412 and a corresponding DST client module of a plurality of DST client modules 1-N of the plurality of access units 1-N. The decentralized agreement module 412 may be implemented utilizing the decentralized agreement module 350 of FIG. 40A. Each DST client module may be implemented utilizing the DST client module 34 of FIG. 1 . Hereafter, each access unit may be interchangeably referred to as a DSN unit and the plurality of access units may be interchangeably referred to as a plurality of DSN units.

The DST execution unit set 410 includes a set of DST execution units 1-n. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 . Hereafter, each DST execution unit may be interchangeably referred to as a storage unit and the DST execution unit set 410 may be interchangeably referred to as a set of storage units and/or a DSN memory of the DSN. The DSN functions to adjust timing of storing of data in the DST execution unit set 410, where a data object is divided into a plurality of data segments, each data segment is dispersed storage error encoded to produce a set of encoded data slices of a plurality of sets of encoded data slices, and a plurality of sets of encoded data slices are stored in the DST execution unit set 410.

FIG. 41A illustrates steps of an example of operation of the adjusting of timing of the storing of the data where access units 1-2 received stored data object A requests of revisions 4 and 3 respectively. Each of the DST client modules 1 and 2 dispersed storage error encodes received revisions of the data object A to produce a corresponding at least one set of encoded data slices for storage in the set of DST execution units 1-n and generates a corresponding range of DSN addresses (e.g., at least one set of slice names). Having produced the at least one set of encoded data slices, a DSN unit of the plurality of DSN units sends a write request to DSN memory, where the write request includes the range of DSN addresses. For example, at time=t1, substantially simultaneously, the DST client module 1 sends, via the network 24, a write request A-1 to the set of DST execution units and the DST client module 2 sends a write request A-2 to the set of DST execution units.

Having sent the write request, in response to the write request the DSN unit receives an error message indicating that another DSN unit of the plurality of DSN units has current write permission to the DSN memory to the range of DSN addresses. For example, the DST client module 1 receives, via the network 24, an error response A-1 that includes the error message and DST client module 2 receives, via the network 24, a success response A-2 indicating that the access unit 2 has the current write permission to the DSN memory to the range of DSN addresses (e.g., the corresponding set of slice names).

Having received the error message, the DSN unit (e.g., the access unit 1) performs a scoring function using one or more properties of the range of DSN addresses and one or more properties of each of at least some of the plurality of DSN units to produce a scoring resultant. The one or more properties of the range of DSN addresses includes one of an individual DSN address, at least some DSN addresses in the range of DSN addresses, a source name corresponding to a data object, a set of source names corresponding to a set of data objects, an individual slice name, and a range of slice names. The one or more properties of each of the at least some of the plurality of DSN units includes at least one of a DSN unit identifier and a DSN unit weighting factor.

The performing the scoring function includes obtaining the one or more properties of the range of DSN addresses, obtaining the one or more properties of each of the at least some of the plurality of DSN units, and performing a decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant. The obtaining the one or more properties of the range of DSN addresses includes selecting the range of DSN addresses associated with the error message. For example, the DST client module 1 identifies a slice name associated with the error response A-1 as the range of DSN addresses. As another example, the DST client module 1 identifies a range of slice names associated with the error response A-1 as the range of DSN addresses.

The obtaining the one or more properties of each of the at least some of the plurality of DSN units includes a variety of approaches. In a first approach, the DSN unit accesses a centralized system registry to retrieve the one or more properties of the each of the at least some of the plurality of DSN units. For example, the DST client module 1 accesses the centralized system registry and extracts DSN unit identifiers and weighting factors of the DSN units. In a second approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit extracts the one or more properties of the each of the at least some of the plurality of DSN units from the error message. For example, the DST client module 1 extracts the DSN unit identifiers and weighting factors of the DSN units from the error message, when the error message indicates identities of DSN units receiving the error message (e.g., a list of contending access units).

In a third approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit (e.g., access unit 1) determines an expected number of concurrent write requests for a block of DSN addresses that includes the range of DSN addresses (e.g., block is one or more ranges) and selects the expected number of DSN units from the plurality of DSN units to produce the at least some of the plurality of DSN units. The determining the expected number of concurrent write requests includes one of accessing the centralized system registry to determine the expected number (e.g., a predetermined estimated number), randomly selecting the expected number using a random number generation function (e.g., pick a number between 5 and 20), determining the expected number based on historical concurrent write data for the range of DSN addresses (e.g., the DST client module 1 accesses a stark records), and determining the expected number based on information contained in the error message (e.g., a list of contending DSN units).

The selecting the expected number of DSN units includes one of accessing the centralized system registry to identify the selected number of DSN units (e.g., a predetermined list of DSN units based on an identity of the DSN unit), where the selected number of DSN units includes the DSN unit, using a random selection function to identify the selected number of DSN units (e.g., every other DSN unit), determining the selected number of DSN units based on historical concurrent write data for the range of DSN addresses (e.g., the DST client module 1 accesses the historical records), and determining the selected number of DSN units based on information contained in the error message (e.g., interpreting identifiers of the contending DSN units).

Having obtained the one or more properties of the range of DSN addresses and the one or more properties of each of the at least some of the plurality of DSN units, the DSN unit performs the decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant. For example, the DST client module 1 issues a ranked scoring information request 414 to the decentralized agreement module 412 where the ranked scoring information request 414 includes the obtained one or more properties of the range of DSN addresses (e.g., slice names associated with the error message) and the obtained one or more properties of each of the at least some of the plurality of DSN units (e.g., identifiers of five of the access units and weighting factors of the five access units).

The decentralized agreement module 412 performs the decentralized agreement protocol function based on the ranked scoring information request 414 to produce ranked scoring information 416 as the scoring resultant. The performing of the decentralized agreement protocol function is discussed in greater detail with reference to FIG. 41B.

Having produced the scoring resultant, the DSN unit interprets the scoring resultant to determine a re-write requesting protocol for resending the write request to the DSN memory. The interpreting the scoring resultant to determine the re-write requesting protocol includes determining a re-write ranking of the DSN unit from the scoring resultant (e.g., ranking versus other DSN units of the contending DSN units) and further includes one of a variety of approaches to determine timing for the resending of the write request. A first approach includes the DSN unit determining, based on the re-write ranking, a time slot of a plurality of times slots for resending the write request (e.g., the access unit 1 determines that a fourth time slot of the plurality of timeslots for resending is associated with the access unit 1). A second approach includes the DSN unit determining, based on the re-write ranking, a wait period before resending the write request (e.g., the excess unit 1 determines that a fourth standard deviation of a wait period is associated with the access unit 1). When the re-write ranking is of highest ranking, a third approach includes the DSN unit substantially continually resending the write request until a write success response is received by the DSN unit (e.g., the DST client module 1 continually resends the write request A-1 until receiving the write access response). A fourth approach includes the DSN unit determining, based on the re-write ranking, a resending interval for resending the write request until the write success response is received by the DSN unit (e.g., the DST client module 1 selects a fourth standard interval between retrying sending of the write request A-1).

FIG. 41B is a schematic block diagram of an embodiment of an access unit that includes the DST client module 1 of FIG. 41A and the decentralized agreement module 412 of FIG. 41A. The decentralized agreement module includes a plurality of deterministic functions 1-N, a plurality of normalizing functions 1-N, a plurality of scoring functions 1-N, and a ranking function 420. Each deterministic function may be implemented utilizing the deterministic function of FIG. 40A. Each normalizing function may be implemented utilizing the normalizing function of FIG. 40A. Each scoring function may be implemented utilizing the scoring function of FIG. 40A. The ranking function 420 may be implemented utilizing the ranking function 352 of FIG. 40A.

FIG. 41B illustrates further steps of the example of operation of the adjusting of timing of the storing of the data, and in particular performing of the decentralized agreement protocol function, where the DST client module 1 receives the error response A1 and generates the ranked scoring information request 414 to include a slice name 418 the obtained one or more properties of the range of DSN addresses and access unit identifiers (e.g., identifiers of access units 1-N when the at least some of the plurality of DSN units includes all of the DSN units) and access unit weights (e.g., weighting factors of access units 1-N when the at least some of the plurality of DSN units includes all of the DSN units) of the obtained one or more properties of each of the at least some of the plurality of DSN units. Having generated the ranked scoring information request 414, the DST client module 1 sends the ranked scoring information request 414 to the decentralized agreement module 412.

Each of the deterministic functions performs a first function based on an identifier of one of the at least some of the plurality of DSN units and the one or more properties of the range of DSN addresses to produce an interim result. For example, the deterministic function 2 performs a deterministic function on the slice name 418 and the access unit 2 identifier to produce an interim result 2. Each of the normalizing functions normalizes a corresponding interim result to produce a normalized result. For example, normalizing function 2 normalizes the interim result 2 to produce a normalized interim result 2. Each scoring function performs a second function based on the normalized result and a weighting factor for the one of the at least some of the plurality of DSN units to produce a score. For example, the scoring function 2 performs a scoring function on the normalized interim result 2 using the access 2 weight to produce a score 2 of a plurality of scores (e.g., 1-N). The ranking function 420 ranks the scores for each of the at least some of the plurality of DSN units to produce the ranked scoring information 416 as the scoring resultant. For example, the ranking function 420 indicates that a tenth DSN unit is associated with a highest score, a sixth DSN unit is associated with a second highest score, etc.

The DST client module 1 interprets the ranked scoring information 416 to determine the re-write requesting protocol. Having produced the re-write requesting protocol, the DST client module 1 resends the write request A-1 at t4 when the ranked scoring information 416 indicates that the access unit 1 is associated with resending the write request within a fourth time interval (e.g., at t4).

FIG. 41C illustrates further steps of the example of operation of the adjusting of timing of the storing of the data and where, having produced the re-write requesting protocol, the DSN unit resends the write request in accordance with the re-write requesting protocol. For example, the DST client module 1 resends, via the network 24, the write request A-1 at t4 to the DST execution unit set 410 when the ranked scoring information 416 indicates that the access unit 1 is associated with resending the write request within a fourth time interval (e.g., at t4).

Alternatively, or in addition to the steps discussed above, the DSN unit may utilize a first number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a first scoring resultant as the scoring resultant, interpret the first scoring resultant to determine a first re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the first re-write requesting protocol. When a second error message is received in response to the resending the write request in accordance with the first re-write requesting protocol, the DSN unit utilizes a second number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a second scoring resultant as the scoring resultant, interprets the second scoring resultant to determine a second re-write requesting protocol for resending the write request to the DSN memory, and resends the write request in accordance with the second re-write requesting protocol FIG. 41D is a flowchart illustrating an example of adjusting timing of storing data. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-40D, 41A-C, and also FIG. 41D. The method begins or continues at step 430 where a processing module of a computing device (e.g., a dispersed storage network (DSN) unit) of one or more computing devices of a DSN (e.g., one or more DSN units and other computing devices) sends a write request to DSN memory, where the write request includes a range of DSN addresses (e.g., a set of slice names).

In response to the write request, the method continues at step 432 where the processing module receives an error message indicating that another DSN unit of the plurality of DSN units has current write permission to the DSN memory to the range of DSN addresses. The method continues at step 434 where the processing module performs a scoring function using one or more properties of the range of DSN addresses and one or more properties of each of at least some of the plurality of DSN units to produce a scoring resultant.

The performing the scoring function includes obtaining the one or more properties of the range of DSN addresses, obtaining the one or more properties of each of the at least some of the plurality of DSN units, and performing a decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant.

The obtaining the one or more properties of each of the at least some of the plurality of DSN units includes a variety of approaches. In a first approach, the processing module accesses a centralized system registry to retrieve the one or more properties of the each of the at least some of the plurality of DSN units, where the one or more properties of the each of the at least some of the plurality of DSN units includes at least one of a DSN unit identifier and a DSN unit weighting factor. In a second approach of the obtaining the one or more properties of each of the at least some of the plurality of DSN units, the processing module extracts the one or more properties of the each of the at least some of the plurality of DSN units from the error message.

The obtaining the one or more properties of each of the at least some of the plurality of DSN units may further include determining an expected number of concurrent write requests for a block of DSN addresses that includes the range of DSN addresses and selecting the expected number of DSN units from the plurality of DSN units to produce the at least some of the plurality of DSN units. The determining the expected number of concurrent write requests includes one of the processing module accessing a centralized system registry to determine the expected number, the processing module randomly selecting the expected number using a random number generation function, the processing module determining the expected number based on historical concurrent write data for the range of DSN addresses, and the processing module determining the expected number based on information contained in the error message.

The selecting the expected number of DSN units includes one of the processing module accessing the centralized system registry to identify the selected number of DSN units, where the selected number of DSN units includes the DSN unit, the processing module using a random selection function to identify the selected number of DSN units, the processing module determining the selected number of DSN units based on historical concurrent write data for the range of DSN addresses, and the processing module determining the selected number of DSN units based on information contained in the error message.

The performing the decentralized agreement protocol function on the obtained one or more properties of the range of DSN addresses and the obtained one or more properties of each of the at least some of the plurality of DSN units to produce the scoring resultant includes, for each of the at least some of the plurality of DSN units, the processing module performing a first function (e.g., a deterministic function) based on an identifier of one of the at least some of the plurality of DSN units and the one or more properties of the range of DSN addresses to produce an interim result, the processing module normalizing the interim result to produce a normalized result, and the processing module performing a second function (e.g., a scoring function) based on the normalized result and a weighting factor for the one of the at least some of the plurality of DSN units to produce a score. Having produced the scores, the processing module ranks the scores for each of the at least some of the plurality of DSN units to produce the scoring resultant.

The method continues at step 436 where the processing module interprets the scoring resultant to determine a re-write requesting protocol for resending the write request to the DSN memory. The interpreting includes the processing module determining a re-write ranking of the DSN unit from the scoring resultant and further includes one of the processing module determining, based on the re-write ranking, a time slot of a plurality of times slots for resending the write request, the processing module determining, based on the re-write ranking, a wait period before resending the write request, when the re-write ranking is of highest ranking, the processing module substantially continually resending the write request until a write success response is received by the DSN unit, and the processing module determining, based on the re-write ranking, a resending interval for resending the write request until the write success response is received by the DSN unit.

The method continues at step 438 where the processing module resends the write request in accordance with the re-write requesting protocol. Alternatively, or in addition to the steps discussed above, the processing module may utilize a first number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a first scoring resultant as the scoring resultant, interpret the first scoring resultant to determine a first re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the first re-write requesting protocol. When a second error message is received in response to the resending the write request in accordance with the first re-write requesting protocol, the processing module utilizes a second number of DSN units as the at least some of the plurality of DSN units when performing the scoring function to produce a second scoring resultant as the scoring resultant, interprets the second scoring resultant to determine a second re-write requesting protocol for resending the write request to the DSN memory, and resend the write request in accordance with the second re-write requesting protocol.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the dispersed storage network or by other devices. In addition, at least one memory section (e.g., a non-transitory computer readable storage medium) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices of the dispersed storage network (DSN), cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 42A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) client module 34 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution unit set 470. The DST client module 34 includes the outbound DST processing 80 and the inbound DST processing 82 of FIG. 3 . The outbound DST processing 80 includes the dispersed storage (DS) error encoding 112 of FIG. 4 and a slice combiner 472. The inbound DST processing 82 includes the DS error decoding 182 of FIG. 13 and a slice de-combiner 474. The DST execution unit set 470 includes a set of DST execution units 1-n. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to access stored data. In an example of operation of the accessing of the stored data, the DS error encoding 112 dispersed storage error encodes a data segment of data in accordance with dispersal parameters to produce a set of encoded data slices 476. The dispersal parameters includes one or more of an information dispersal algorithm (IDA) width, a decode threshold number, and an identifier of an encoding matrix. For example, the DS error encoding 112 utilizes dispersal parameters that includes an IDA width of 12 based on a system registry lookup for a vault associated with the data.

The slice combiner 472 determines a number of storage locations for storage of the set of encoded data slices. The determining may be based on one or more of initiating a query, receiving a query response, performing a lookup, and receiving storage location instructions. For example, the slice combiner 472 determines to utilize eight storage locations when the DST execution unit set includes eight DST execution units 1-8. For instance, the number of storage locations is less than the IDA width when the number of storage locations is eight and the IDA width is 12.

Having determined the number of storage locations, for each storage location, the slice combiner 472 combines a portion of at least two encoded data slices to produce a combined slice 478 for storage at the storage location in accordance with a combining approach when the number of storage locations is less than the IDA width. The combining approach includes one of an even distribution, a weighted distribution, and a predetermined distribution. For example, the slice combiner 472 combines halves of encoded data slices such that each storage location will receive three halves of an encoded data slice when the IDA width is 12 and the number of storage locations is eight. For instance, the slice combiner combines both halves of encoded data slice 1 with a first half of encoded data slice 2 to produce a first combined slice, combines a second half of encoded data slice 2 with both halves of encoded data slice 3 to produce a second combined slice, etc. Many other such combinations are possible.

For each combined slice, the outbound DST processing 80 sends, via the network 24, the combined slice 478 to the corresponding DST execution unit for storage. For example, the outbound DST processing generates a write slice request that includes the first combined slice and sends, via the network 24, the first combined slice to the DST execution unit 1 for storage. The generating of the write request may further include generating one or more slice names associated with the at least two encoded data slices utilized to produce the combined slice.

When retrieving the data, the inbound DST processing 82 receives combined slices 478 from the DST execution unit set. For example, the inbound DST processing 82 issues, via the network 24, read slice requests to the DST execution unit set and extracts the combined slices 478 from received read slice responses. For each received combined slice, the slice de-combiner 474 performs a de-combiner function on the received combined slice to reproduce the portions of the at least two encoded data slices.

Having produced the portions of the at least two encoded data slices, the slice de-combiner 474 aggregates portions of combined encoded data slices to reproduce the set of encoded data slices 476. Alternatively, the slice de-combiner produces at least a decode threshold number of encoded data slices of the set of encoded data slices. The DS error decoding 182 dispersed storage error decodes the decode threshold number of encoded data slices of the reproduced set of encoded data slices 476 to produce a recovered data segment.

FIG. 42B is a flowchart illustrating an example of accessing stored data. The method begins or continues at step 480 where a processing module (e.g., of a distributed storage and task (DST) client module), when storing data, encodes a data segment of data to produce a set of encoded data slices, where the set of encoded data slices includes an information dispersal algorithm (IDA) width number of encoded data slices. The method continues at step 482 where the processing module determines a number of storage locations, where the number of storage locations is less than the IDA width number. The determining includes at least one of interpreting a system registry, initiating a query, and interpreting a query response.

For each storage location, the method continues at step 484 where the processing module combines a portion of at least two encoded data slices of the set of encoded data slices to produce a combined slice for storage in the storage location. The combining is in accordance with a combining approach. For each combined slice, the method continues at step 486 where the processing module sends the combined slice to the storage location for storage.

When retrieving the data, the method continues at step 488 where the processing module obtains combined slices associated with a set of encoded data slices. For example, the processing module issues read slice requests to the storage locations, where the read slice requests includes slice names associated with the portions of the encoded data slices, and receives read slice responses that includes the combined slices.

For each combined slice, the method continues at step 490 where the processing module de-combines the combined slice to reproduce the portions of the at least two encoded data slices. For example, the processing module de-combines the combined slice in accordance with the combining approach to reproduce the portions of the at least two encoded data slices.

The method continues at 492 where the processing module aggregates portions of common encoded data slices to reproduce at least some encoded data slices of the set of encoded data slices. The method continues at step 494 where the processing module decodes a decode threshold number of the reproduced at least some encoded data slices of the set of encoded data slices to produce a recovered data segment.

FIG. 43A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution (EX) unit set 500. The DST execution unit set 500 includes a set of DST execution units 1-n. Each DST execution unit includes one or more memories 1-2 and the processing module 84 of FIG. 3 .

The DSN functions to migrate data from a first storage format to a second storage format. In an example of operation of the migration of the data, the processing module 84 of DST execution unit 1 determines to update a storage format to a second storage format for a plurality of encoded data slices stored in a first memory utilizing a first storage format. The determining includes at least one of interpreting a request, identifying a data type, receiving new storage configuration information, detecting a storage efficiency level that compares unfavorably to a threshold level, and detecting an unfavorable performance level.

Having determined to update the storage format, the processing module 84 initiates migration of the plurality of encoded data slices from the first memory to a second memory, where the encoded data slices are stored in the second memory using the updated storage format. Alternatively, the first and second memories are a common memory. For example, the processing module 84 retrieves an encoded data slice stored in the first memory utilizing the first storage format and stores the retrieved encoded data slice in the second memory utilizing the second storage format.

While migrating the plurality of encoded data slices, the processing module 84 updates a rate of migration based on a DST execution unit activity level. The updating includes determining the updated DST execution unit activity level. For example, the processing module 84 monitors access requests 502 and access responses 504 associated with one or more of rebuilding encoded data slices, writing the encoded data slices, retrieving the encoded data slices, listing the encoded data slices, performing maintenance tasks, performing balancing tasks, and any other activities of the DST execution unit. For example, the processing module 84 lowers the rate (e.g., including pausing) of migration when detecting a higher than average DST execution unit activity level.

FIG. 43B is a flowchart illustrating an example of migrating data from a first storage format to a second storage format. The method begins or continues at step 506 where a processing module (e.g., of a distributed storage and task (DST) execution unit, a storage unit) identifies a first storage format utilized to store a first plurality of encoded data slices in a first memory of the storage unit of a set of storage units, where a data segment is dispersed storage error encoded to produce a set of encoded data slices that are stored in the set of storage units. The identifying includes at least one of performing a lookup, initiating a query, interpreting a query response, receiving an error message, and detecting a storage inefficiency.

The method continues at step 508 where the processing module determines to utilize another storage format for storage of the first plurality of encoded data slices. The determining includes at least one of detecting the storage inefficiency, interpreting the error message, and detecting a favorable comparison of an available storage level to an available storage threshold level. The method continues at step 510 where the processing module selects a second storage format as the other storage format for storage of the first plurality of encoded data slices. The selecting includes at least one of identifying one or more other storage formats, estimating an updated level of storage performance for each of the one or more other storage formats, selecting a storage format as the second storage format that corresponds to a favorable estimated storage performance level (e.g., best or above a threshold level).

The method continues at step 512 where the processing module initiates migration of the first plurality of encoded data slices from storage utilizing the first storage format to storage utilizing the second storage format. The initiating includes retrieving an encoded data slice using the first storage format and storing the retrieved encoded data slice using the second storage format. The retrieving and storing may be with a common memory or different memories.

While migrating the first plurality of encoded data slices, the method continues at step 514 where the processing module updates a rate of migration based on a storage unit activity level. The updating includes one or more of monitoring the storage unit activity level, raising the rate of migration when the storage unit activity level is less than a low threshold level, and lowering the rate of migration when the storage unit activity level is greater than a high threshold level.

FIG. 44A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) the distributed storage and task (DST) client module 34 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution (EX) unit set 520. The DST client module 34 includes a key derivation function 522, a blinded password generator 526, a random number generator 524, a key re-generator 528, a decryptor 530, and the dispersed storage (DS) error decoding 182 of FIG. 13 . The DST execution unit set 520 includes a set of DST execution units 1-n. Each DST execution unit includes the memory 88 of FIG. 3 and the processing module 84 of FIG. 3 .

The DSN functions to recover data stored in the DST execution unit set 520. In an example of operation of the recovering of the stored data, the key derivation function 522 performs a key derivation algorithm on a password 532 to produce a key 534. The key derivation algorithm generates the key 534 based on the password 532 in accordance with a number of desired bits for the key 534. Examples of the key derivation algorithm includes at least one of industry algorithms PBKDF2, bcrypt, and scrypt. Such a key derivation algorithm may take 100 ms or more to process the password 532, thus providing an anti-hacking system performance improvement.

The random number generator 524 generates a set of random numbers b1-bn. The blinded password generator 526 generates a set of blinded passwords 1-n based on the key 534 and the set of random numbers b1-bn. For example, the blinded password generator 526 generates a first blinded password in accordance with the formula of: blinded password 1=[[MGF(KEY)]{circumflex over ( )}2]{circumflex over ( )}b1 modulo p; where MGF is a mask generating function, and p is a fixed number related to another fixed number q in accordance with a formula q=(p−1)/2. Having generated the set of blinded passwords 1-n, the DST client module 34 sends, via the network 24, the set of blinded passwords 1-n to the set of DST execution units 1-n.

The processing module 84 of each DST execution unit generates corresponding confidential information based on a received blinded password, a corresponding recovered random number of a set of original random numbers e1-en, and a retrieved encrypted slice, where a data segment of data was previously dispersed storage error encoded to produce a set of encoded data slices and where each encoded data slice was encrypted using a corresponding key of a set of keys to produce an encrypted slice of a set of encrypted slices and where the set of keys were generated based on the password and the set of original random numbers e1-en and that includes the corresponding recovered random number. For example, the DST processing module 84 of DST execution unit 1 recovers a first encrypted slice from the memory 88 of the DST execution unit 1, recovers a first original random number e1, generates a first passkey of a set of passkeys 1-n in accordance with a formula of: passkey 1=blinded password re′ modulo p, and aggregates the first encrypted slice and the blinded password 1 to produce confidential information 1.

Having generated the corresponding confidential information, each DST execution unit sends the corresponding information to the DST client module 34. For example, the processing module 84 of the DST execution unit 1 sends, via the network 24, the confidential information 1 to the DST client module 34. The DST client module 34 receives at least a decode threshold number of a set of confidential information 1-n.

Having received the confidential information 1-n, the key re-generator 528 regenerates the set of keys 1-n based on received passkeys 1-n and the set of random numbers b1-bn when receiving passkeys 1-n. For example, the key re-generator 528 regenerates a first key in accordance with a formula of: key 1=passkey 1{circumflex over ( )}v1 modulo p, where v1 is derived based on a relationship of b1*v1=1 modulo q.

The decryptor 530 decrypts received encrypted slices 1-n using the regenerated set of keys 1-n to reproduce the set of encoded data slices 1-n. The DS error decoding 182 dispersed storage error decodes a decode threshold number of reproduced encoded data slices of the set of encoded data slices 1-n to produce recovered data 536.

FIG. 44B is a flowchart illustrating an example of recovering data. The method begins or continues at step 538 where a processing module (e.g., of a distributed storage and task (DST) client module) performs a key derivation function on a password to produce a key. The method continues at step 540 where the processing module issues a set of blinded passwords to a set of storage units, where the blinded passwords are generated based on the key. For example, the processing module generates a first blinded password in accordance with a formula of: blinded password 1=[[MGF(KEY)]{circumflex over ( )}2]{circumflex over ( )}b1 modulo p, where b1 is a random number of a set of random numbers.

The method continues at step 542 where the processing module receives at least a decode threshold number of confidential information responses, where each response includes an encrypted slice and an associated passkey, where a first passkey is generated by a corresponding storage unit in accordance with a formula of: passkey 1=bpass1{circumflex over ( )}e1 modulo p; where e1 is a recovered random number associated with the encrypted slice and a corresponding original random number.

The method continues at step 544 where the processing module regenerates a set of keys using passkeys of the confidential information. For example, the processing module generates a first key of the set of keys in accordance with a formula of key 1=passkey1{circumflex over ( )}v1 modulo p; where b1*v1=1 modulo q and where q=(p−1)/2.

The method continues at step 546 where the processing module decrypts a set of encrypted slices of the confidential information using the set of keys to reproduce a set of encoded data slices. For example, the processing module decrypts the encrypted slice 1 using the first key 1 to produce an encoded data slice 1 of the set of encoded data slices. The method continues at step 548 where the processing module dispersed storage error decodes a decode threshold number of the set of reproduced encoded data slices to produce recovered data.

FIG. 45A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and at least two DST execution (EX) unit tier storage pools 2, and 4. The DST processing unit 16 includes the DST client module 34 of FIG. 1 . Each DST execution unit tier storage pool includes a set of DST execution units 1-n. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to migrate data stored in a first storage pool tier level to a second storage pool tier level, where data is dispersed storage error encoded to produce a plurality of sets of encoded data slices that is stored in the set of DST execution units of the first storage pool tier level. For example, a first data object A is dispersed storage error encoded to produce a plurality of sets of encoded data slices that are stored in the set of DST execution units of the DST execution unit tier 2 storage pool. For instance, slices A-1-1 to A-1-M are stored in DST execution unit 1, slices A-2-1 to A-2-M are stored in DST execution unit 2, etc.

In an example of operation of the migration, the DST client module 34 determines to update a storage pool tier level for data stored in the first storage pool, where the storage pool is associated with the first storage pool tier level. The determining includes at least one of interpreting an account update, identifying the data based on an account identifier, detecting nonpayment for storage services, detecting lower payment for storage services, detecting higher payment for storage services, interpret a storage pool tier level schedule, receiving a request, detecting data access inactivity, and detecting an unfavorable storage performance level. For example, the DST client module 34 identifies the encoded data slices associated with the data object A as the data stored in the first storage pool for updating of the storage pool tier level when payment for storage services of the data object A are delinquent.

Having determined to update the storage pool tier level, the DST client module 34 determines a second storage pool tier level based on the determining to update the storage pool tier level. The determining may be based on one or more of a comparison of actual payment for storage services to terms of payment for storage services. For example, the DST client module 34 determines to utilize a tier 4 storage pool for storage of the encoded data slices of the data object A when the payment for the storage services of the data object A are delinquent and the tier 4 storage pool is associated with low payment or nonpayment (e.g., a long-term storage service class, a storage service with lower retrieval reliability).

Having determined the second storage pool tier level, the DST client module 34 selects a set of DST execution units associated with the second storage pool. The selecting may be based on one or more of a request, a lookup of available sets of storage units and associated tier levels, a storage costs level, a round-robin selection approach, initiating a query, and interpreting a query response. For example, the DST client module selects the DST execution unit tier 4 storage pool when selecting the tier 4 storage pool level and the DST execution unit tier 4 storage pool is available with available storage capacity.

Having selected the second storage pool, the DST client module 34 facilitates migration of one or more sets of encoded data slices associated with the data from the first storage pool to the second storage pool. For example, the DST client module 34 issues access requests 550, via the network 24, to the DST execution unit tier 2 storage pool to retrieve the slices associated with the data object A, receives access responses 552 that includes the requested slices, and issues further access requests 550 to the DST execution unit tier 4 storage pool, where the further access requests 550 includes the slices associated with the data object A.

Having facilitated the migration of the one or more sets of encoded data slices, the DST client module 34 updates at least one of a DSN directory and a dispersed hierarchical index to associate the one or more sets of encoded data slices with the second storage pool and to disassociate the one or more sets of encoded data slices from the first storage pool. For example, the DST client module 34 updates an index entry of the dispersed hierarchical index to include a source name associated with the data object A, a data name for the data object A, and an identifier of the DST execution unit tier 4 storage pool.

FIG. 45B is a flowchart illustrating an example of migrating data from a first storage pool tier level to a second storage pool tier level. The method begins or continues at step 554 where a processing module (e.g., of a distributed storage and task (DST) client module) determines to update a storage pool tier level for data stored in a first storage pool. The determining may be based on one or more of interpreting a request, interpreting an account update, identifying the data based on an account identifier, detecting a payment mismatch for storage services, and detecting an unfavorable storage performance.

The method continues at step 556 where the processing module determines a second storage pool tier level, where the determining may be based on one or more of a level commensurate with the updated account, the payment mismatch, and a desired storage performance level. The method continues at step 558 where the processing module selects a second storage pool based on the second storage pool tier level. For example, the processing module identifies one or more storage pools associated with the second storage pool tier level, selects a storage pool based on one or more of availability, available storage space, pricing, a random selection, a preferred storage pool, and an estimated performance for a user.

The method continues at step 560 where the processing module identifies one or more sets of encoded data slices associated with the data stored in the first storage pool. The identifying includes at least one or more of accessing one or more of a dispersed storage network directory and a dispersed hierarchical index to identify one or more DSN address range that is associated with the data, identify slice names and the DSN address ranges, and identify stored encoded data slices associated with the identified slice names.

The method continues at step 562 where the processing module facilitates migration of the identified one or more sets of encoded data slices from the first storage pool to the second storage pool. For example, the processing module retrieves the identified one or more sets of encoded data slices from the first storage pool and stores the one or more sets of encoded data slices and the second storage pool.

The method continues at step 564 where the processing module associates the identified one or more sets of encoded data slices with the second storage pool. The associating includes updating at least one of the DSN directory and the dispersed hierarchical index to associate the identified slice names with the second storage pool and to disassociate the identified slice names from the first storage pool.

FIG. 46A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution (EX) unit storage vault 570. The DST processing unit 16 includes the DST client module 34 of FIG. 1 . The DST execution unit storage vault 570 includes at least one set of DST execution units 1-n. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to access data stored in a plurality of containers 1-C (e.g., virtual containers) within the set of DST execution units 1-n, where data is dispersed storage error encoded to produce a plurality of sets of encoded data slices that are stored in at least one container within the set of DST execution units 1-n. For example, for a data object A, a plurality of sets encoded data slices, including A-1-1, A-2-1, through A-n-1 for a first set through an Mth set A-1-M, A-2-M, through A-n-M are stored in a first container within the set of DST execution units 1-n. In an example of operation, the DST client module 34 obtains a request (e.g., store, retrieve) to access the data associated with the first container of the DST execution unit storage vault.

Having obtained the request, the DST client module 34 identifies a first header object associated with the first container. The determining may include one or more of a performing local lookup, interpreting an index entry of a dispersed hierarchical index, interpreting a DSN directory entry, and may be further based on one or more of an identifier of a requesting entity, an identifier of the data, performing a container identifier look up based on the identifier of the requesting entity, and performing a lookup of a DSN address of the first header object based on the container identifier. For example, the DST client module 34 performs a dispersed hierarchical index lookup utilizing the identifier of the data to recover an index entry, extracts the container identifier of container 1 from the index entry, performs another dispersed hierarchical index lookup utilizing the container identifier to recover another index entry, and extracts the DSN address of the first header object from the recovered other index entry for the container 1.

Having identified the first header object, the DST client module 34 recovers the first header object from the DSN memory. For example, the DST client module 34 issues, via the network 24, access requests 572 to the set of DST execution units 1-n using the DSN address of the first header object, receives access responses 574, and decodes a decode threshold number of encoded headers slices of the access responses to reproduce the first header object.

Having reproduced the first header object, the DST client module 34 further processes the request to access the data utilizing a first encryption key extracted from the first header object. For example, the DST client module 34, when storing the data, encrypts the data using the first encryption key to produce encrypted data, dispersed storage error encodes the encrypted data to produce a plurality of sets of encoded data slices, and sends, via the network 24, the plurality of sets of encoded data slices to the set of DST execution units for storage. As another example, the DST client module 34, when retrieving the data, recovers a decode threshold number of encoded data slices for each set of encoded data slices from the set of DST execution units, decodes, for each set of encoded data slices, each decode threshold number of encoded data slices to produce recovered encrypted data, and decrypts the recovered encrypted data using the first encryption key to produce recovered data.

Having stored the data, the DST client module 34 may determine to make the data associated with the first container of the DST execution unit storage vault unusable. The determining may be based on one or more of receiving a data deletion request for the first container, interpreting a data deletion schedule, and detecting that an available storage space is less than a low available storage space threshold level. When determining to make the data associated with the first container unusable, the DST client module 34 identifies the first data object associated with the first container. Having identified the first header object, the DST client module 34 facilitates disabling use of the first encryption key of the first header object.

As a specific example, the DST client module 34 recovers the first header object, deletes the first encryption key to produce an updated first header object, and stores the updated first header object in the set of DST execution units 1-n. The storing includes dispersed storage error encoding the updated first header object to produce a set of updated first header object slices and sending, via the network 24, the set of updated first header object slices to the set of DST execution units 1-n for storage.

As another specific example, the DST client module 34 recovers the first header object, generates a copy of the first header object, stores the copy of the first header object in another data object, deletes the first encryption key from the first header object to produce the updated first header object, and stores the updated first header object in the set of DST execution units 1-n. As yet another specific example, the DST client module 34 recovers the first header object, encrypts the first encryption key using a secret key to produce an obfuscated first encryption key, replaces the first encryption key with the obfuscated first encryption key within the recovered first header object to produce the updated first header object, and stores the updated first header object in the set of DST execution units 1-n.

FIG. 46B is a flowchart illustrating an example of accessing data within a vault storage container of a dispersed storage network (DSN). The method begins or continues at step 576 where a processing module (e.g., of a distributed storage and task (DST) client module) receives a request to access data associated with a first container of a common storage vault of a DSN memory. The receiving includes at least one of receiving a retrieval request and receiving a storage request.

The method continues at step 578 where the processing module identifies a first header object associated with the first container. For example, the processing module identifies the common storage vault and a container ID based on one or more of a requesting entity identifier and an identifier of the data; and performs a lookup using the container ID to obtain a DSN address of the first header object.

The method continues at step 580 where the processing module recovers the first header object from the DSN memory. For example, the processing module issues read slice requests to the DSN memory using the DSN address of the first header object, receives header slices, and dispersed storage error decodes a decode threshold number of the header slices to reproduce the first header object.

The method continues at step 582 where the processing module facilitates execution of the request to access the data utilizing a first encryption key of the first header object. As a storage example, the processing module extracts the first encryption key from the first header object, encrypts the data using the extracted first encryption key to produce encrypted data, dispersed storage error encodes the encrypted data to produce slices, and sends the slices to a set of storage units associated with the first container. As a retrieval example, the processing module extracts the first encryption key from the first header object, recovers slices from the first container of the set of storage units, dispersed storage error decodes the recovered slices to reproduce the encrypted data, and decrypts the encrypted data using the extracted first encryption key to reproduce the data.

The method continues at step 584 where the processing module determines to make all data associated with the first container of the common storage vault unusable. The determining may be based on one or more of receiving a delete container request, receiving a hide container request, receiving a make unusable request, interpreting a storage error, and interpreting an error message.

The method continues at step 586 where the processing module identifies the first data object associated with the first container. For example, the processing module performs a lookup using the container identifier to obtain the DSN address of the first header object. The method continues at step 588 where the processing module facilitates disabling use of the first encryption key of the first header object for accessing all the data. As a specific example, the processing module permanently deletes the first encryption key. As another example, the processing module moves the first encryption key to another storage location. As yet another example, the processing module replaces the first encryption key with an encrypted version of the first encryption key utilizing a secret key. Alternatively, or in addition to, the processing module deletes encoded data slices associated with the first container (e.g., for each set of encoded data slices associated with each data object associated with the first container).

FIG. 47A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution (EX) unit set 590. The DST processing unit 16 includes the DST client module 34 of FIG. 1 . The DST execution unit set 590 includes a set of DST execution units 1-n. Each DST execution unit includes a plurality of memories 1-R. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to select a set of memories for storage of one or more sets of encoded data slices of data for storage, where the set of DST execution units includes a plurality of sets of memories that includes the selected set of memories, where each set of memories is associated with a unique DSN address range of a plurality of DSN address ranges associated with the set of DST execution units. In an example of operation of the selecting of the set of memories, the DST client module 34 obtains performance history from each DST execution unit of the DST execution unit set 590, where the performance history includes performance level history of each memory device of the plurality of memory devices associated with the DST execution unit.

The performance level history includes one or more of an access frequency level, an access latency level, a throughput level, an available storage space level, a utilized storage space level, and a storage capacity level. The obtaining includes at least one of issuing a performance history request, receiving a performance history response, interpreting an error message, and performing a lookup. For example, the DST client module 34 receives performance history 1-n from the DST execution unit set in response to issuing, via the network 24, the performance history request.

Having obtained the performance history, the DST client module 34 obtains a set of encoded data slices for storage in the set of DST execution units 1-n. The obtaining includes at least one of receiving the set of encoded data slices and dispersed storage error encoding a data segment of the data to produce the set of encoded data slices.

Having obtained the set of encoded data slices, the DST client module 34 selects a set of memory devices of the plurality of sets of memory devices based on the performance history in accordance with a selection approach. As a specific example, the selection approach includes identifying a set of memory devices associated with the at least a threshold number (e.g., a write threshold number) of memory devices with favorable performance histories (e.g., similar history, above a threshold level, fastest, most available space, at least active).

Having selected the set of memory devices, the DST client module 34 generates a set of slice names corresponding to the selected set of memory devices. As a specific example, the DST client module 34 identifies a DSN address range associated with a set of memory devices based on lookup, generates a source name associated with the identify DSN address range that includes the generated source name, and generates the set of slice names to include the source name.

Having generated the set of slice names, the DST client module 34 generates a set of write slice requests 592 that includes the set of slice names of the set of encoded data slices. Having generated the set of write slice requests 592, the DST client module 34 sends, via the network 24, the set of write slice requests 592 to the DST execution unit set to store the set of encoded data slices in the selected set of memory devices.

FIG. 47B is a flowchart illustrating an example of selecting a set of memory devices. The method begins or continues at step 594 where a processing module (e.g., of a distributed storage and task (DST) client module) obtains performance history from each storage unit of a set of storage units. The obtaining includes at least one of issuing a set of performance history requests to the set of storage units, receiving performance history responses from at least some of the storage units, interpreting an error message, and performing a lookup.

The method continues at step 596 where the processing module obtains a set of encoded data slices for storage in a set of memory devices of a plurality of sets of memory devices of the set of storage units. The obtaining includes at least one of receiving the set of encoded data slices and dispersed storage error encoding a data segment of data to produce the set of encoded data slices.

The method continues at step 598 where the processing module selects the set of memory devices of the plurality of sets of memory devices of the set of storage units based on the performance history of each storage unit. For example, the processing module identifies the set of memory devices associated with at least a threshold number of memory devices that exhibit favorable performance histories in accordance with a selection scheme. The selection schemes includes one or more of selecting similarly performing memory devices, selecting memory devices associated with the performance level above a performance threshold level, selecting fastest performing memory devices, selecting memory devices associated with highest levels of available storage space, and selecting storage units associated with lowest levels of access frequency.

The method continues at step 600 where the processing module generates a set of slice names corresponding to the selected set of memory devices. For example, the processing module generates a source name associated with a dispersed storage network address range affiliated with the set of memory devices and generates the set of slice names to include the generated source name.

The method continues at step 602 where the processing module generates a set of write slice requests that includes the set of encoded data slices and the set of slice names. The method continues at step 604 where the processing module sends the set of write slice requests to the set of storage units to store the set of encoded data slices in the selected set of memory devices.

FIG. 48A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage and task (DST) processing unit 16 of FIG. 1 , the network 24 of FIG. 1 , and a DST execution (EX) unit set 610. The DST processing unit 16 includes the DST client module 34 of FIG. 1 . The DST execution unit set 610 includes a set of DST execution units 1-n. Each DST execution unit may be implemented utilizing the DST execution unit 36 of FIG. 1 .

The DSN functions to update a dispersed hierarchical index and to synchronize the dispersed hierarchical index with stored data. The dispersed hierarchical index may include a dispersed lockless concurrent index in a dispersed data structure that maintains and in-order transportable list of entries indexed by a one or more index keys. For example, for each data object stored in the DST execution unit set as sets of encoded data slices (e.g., A-1-1 to A-1-M, A-2-1 to A-2-M, etc., of a data object A), one or more dispersed hierarchical indexes may be updated to insert a data name associated with the data object A. The dispersed hierarchical index supports an ordered listing of stored data objects in a similar fashion to a directory hierarchy. A data object may be a reference to any number of dispersed hierarchical indexes 1-D. Each dispersed hierarchical index is associated with an index type. Index types include at least one of by name, by time, and by any other common attribute. The index keys are affiliated with the index type. For example, a time based index key is utilized with a dispersed article index associated with a time index type.

An example of operation of the synchronizing of the dispersed hierarchical index with stored data is discussed in greater detail with reference to FIG. 49 . In an example of operation of the updating of the dispersed hierarchical index, the DST client module 34 determines that an update is required to one or more dispersed hierarchical indexes 1-D associated with data stored in the DST execution unit set. The determining may be based on one or more of updating storage of the data, receiving a request, and identifying a storage mismatch.

Having determined that the update is required, the DST client module 34 obtains a performance level of the DST execution unit set. The obtaining includes at least one of interpreting a test result, accessing a historical record, interpreting an error message, and monitoring and access level. Having obtained the performance level of the DST execution unit set, for each of the one or more dispersed hierarchical indexes, the DST client module 34 determines an update schedule based on the performance level of the DST execution unit set and in accordance with an update prioritization approach. The update prioritization approach includes one or more of updating a primary index immediately, updating in accordance with a priority level of each index, always updating all indexes, and updating a portion of an index based on an activity level of the DST execution unit set. As a specific example, the DST client 34 temporarily suspends updating at least some of the one or more dispersed hierarchical indexes associated with the data when access frequency levels of the DST execution units is greater than a high access frequency threshold level (e.g., too busy to update now) in favor of maintaining a temporary local directory to cache pending updates.

Having determined the update schedule, for each of the one or more dispersed hierarchical indexes, the DST client module 34 updates the dispersed hierarchical index in accordance with the update schedule. For example, the DST client module 34, when scheduled, issues access requests 612 to the DST execution unit set to recover an index entry, receives access response 614, updates the index entry (e.g., based on a recently determined required update, based on a cached update) to produce an updated index entry, and issues further access requests 612 to the DST execution unit set to store the updated index entry. Alternatively, the DST client module 34 may generate a new index entry for storage in the DST execution unit set. While updating the one or more dispersed hierarchical indexes, the DST client module 34 may further update the update schedule. The example of the operation of the updating of the dispersed hierarchical index is discussed in greater detail with reference to FIG. 48B.

FIG. 48B is a flowchart illustrating an example of updating a dispersed hierarchical index. The method begins or continues at step 616 where a processing module (e.g., of a distributed storage and task (DST) client module) determines that an update is required to one or more dispersed hierarchical indexes that are associated with data stored in a set of storage units. The determining may be based on one or more of detecting updating of storage of the data, receiving a request to update the data, and identifying a mismatch between a local representation of an index and a representation of the index recovered from the set of storage units.

The method continues at step 618 where the processing module obtains performance levels of the set of storage units. The obtaining includes at least one of initiating a performance test, interpreting a performance test result, accessing a record, interpreting an error message, and interpreting monitored storage unit set access messages.

For each of the one or more dispersed hierarchical indexes, the method continues at step 620 where the processing module determines an update schedule based on the performance level of the storage unit set and in accordance with an update prioritization approach. As a specific example, the processing module identifies the prioritization approach (e.g., performs a lookup, receives a request, determines based on one or more of a data type, a requesting entity identifier, a time of day, and a level of storage unit activity) and implements the prioritization approach to produce the updated schedule.

For each of the one or more dispersed hierarchical indexes, the method continues at step 622 where the processing module updates the dispersed hierarchical index in accordance with the update schedule. As a specific example, the processing module accesses the dispersed hierarchical index from the set of storage units to facilitate synchronization of the accessed dispersed hierarchical index with a local representation of the dispersed article index (e.g., recover, update, store).

FIG. 49 is a flowchart illustrating an example of synchronizing a dispersed hierarchical index with stored data. In the example of operation of the synchronizing of the dispersed hierarchical index with the stored data, the method begins or continues at step 624 where a processing module (e.g., of a distributed storage and task (DST) client module) identifies data objects stored in a DST execution unit set. The identifying includes at least one of accessing a list (e.g., a dispersed storage network directory) to identify a data object and verifying that the data object is stored in the DST execution unit set.

For each data object, the method continues at step 626 where the processing module determines an index key for an associated dispersed hierarchical index. As a specific example, the processing module analyzes the data object to identify a data object attribute (e.g., a name, a timestamp, a metric, a size, a data type, a data owner, etc.) and generates the index key based on the data object attribute.

When the index key is not found within the associated dispersed hierarchical index, the method continues at step 628 where the processing module inserts the index key into an index entry of the associated dispersed hierarchical index. As a specific example, the processing module updates the index entry and stores the updated index entry in the DST execution unit set (e.g., in accordance with an update schedule).

The method continues at step 630 where the processing module identifies index entries of the dispersed hierarchical index. As a specific example, the processing module traverses the dispersed hierarchical index to identify the index entries. For each index entry, the method continues at step 632 where the processing module determines whether a corresponding data object is stored in the DST execution unit set. As a specific example, the processing module accesses a dispersed storage network address associated with the index entry (e.g., issues a list request and receives a list response to determine whether a slice is exist for the data object).

When the corresponding data object is not found within the DST execution unit set, the method continues at step 634 where the processing module indicates an index discrepancy. The indicating includes at least one of indicating an identifier of the index, indicating identifier of the index entry, including a dispersed storage network address of the data object that is not stored in the DST execution unit set, issuing an error message, updating the list, and recovering the data object using another means.

It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is 10 percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., indicates an advantageous relationship that would be evident to one skilled in the art in light of the present disclosure, and based, for example, on the nature of the signals/items that are being compared. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide such an advantageous relationship and/or that provides a disadvantageous relationship. Such an item/signal can correspond to one or more numeric values, one or more measurements, one or more counts and/or proportions, one or more types of data, and/or other information with attributes that can be compared to a threshold, to each other and/or to attributes of other information to determine whether a favorable or unfavorable comparison exists. Examples of such an advantageous relationship can include: one item/signal being greater than (or greater than or equal to) a threshold value, one item/signal being less than (or less than or equal to) a threshold value, one item/signal being greater than (or greater than or equal to) another item/signal, one item/signal being less than (or less than or equal to) another item/signal, one item/signal matching another item/signal, one item/signal substantially matching another item/signal within a predefined or industry accepted tolerance such as 1%, 5%, 10% or some other margin, etc. Furthermore, one skilled in the art will recognize that such a comparison between two items/signals can be performed in different ways. For example, when the advantageous relationship is that signal 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1. Similarly, one skilled in the art will recognize that the comparison of the inverse or opposite of items/signals and/or other forms of mathematical or logical equivalence can likewise be used in an equivalent fashion. For example, the comparison to determine if a signal X>5 is equivalent to determining if −X<−5, and the comparison to determine if signal A matches signal B can likewise be performed by determining −A matches −B or not (A) matches not (B). As may be discussed herein, the determination that a particular relationship is present (either favorable or unfavorable) can be utilized to automatically trigger a particular action. Unless expressly stated to the contrary, the absence of that particular condition may be assumed to imply that the particular action will not automatically be triggered. In other examples, the determination that a particular relationship is present (either favorable or unfavorable) can be utilized as a basis or consideration to determine whether to perform one or more actions. Note that such a basis or consideration can be considered alone or in combination with one or more other bases or considerations to determine whether to perform the one or more actions. In one example where multiple bases or considerations are used to determine whether to perform one or more actions, the respective bases or considerations are given equal weight in such determination. In another example where multiple bases or considerations are used to determine whether to perform one or more actions, the respective bases or considerations are given unequal weight in such determination.

As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, “processing circuitry”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, processing circuitry, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, processing circuitry, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, processing circuitry, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, processing circuitry and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, processing circuitry and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with one or more other routines. In addition, a flow diagram may include an “end” and/or “continue” indication. The “end” and/or “continue” indications reflect that the steps presented can end as described and shown or optionally be incorporated in or otherwise used in conjunction with one or more other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, a quantum register or other quantum memory and/or any other device that stores data in a non-transitory manner. Furthermore, the memory device may be in a form of a solid-state memory, a hard drive memory or other disk storage, cloud memory, thumb drive, server memory, computing device memory, and/or other non-transitory medium for storing data. The storage of data includes temporary storage (i.e., data is lost when power is removed from the memory element) and/or persistent storage (i.e., data is retained when power is removed from the memory element). As used herein, a transitory medium shall mean one or more of: (a) a wired or wireless medium for the transportation of data as a signal from one computing device to another computing device for temporary storage or persistent storage; (b) a wired or wireless medium for the transportation of data as a signal within a computing device from one element of the computing device to another element of the computing device for temporary storage or persistent storage; (c) a wired or wireless medium for the transportation of data as a signal from one computing device to another computing device for processing the data by the other computing device; and (d) a wired or wireless medium for the transportation of data as a signal within a computing device from one element of the computing device to another element of the computing device for processing the data by the other element of the computing device. As may be used herein, a non-transitory computer readable memory is substantially equivalent to a computer readable memory. A non-transitory computer readable memory can also be referred to as a non-transitory computer readable storage medium.

One or more functions associated with the methods and/or processes described herein can be implemented as a large-scale system that is operable to receive, transmit and/or process data on a large-scale. As used herein, a large-scale refers to a large number of data, such as one or more kilobytes, megabytes, gigabytes, terabytes or more of data that are received, transmitted and/or processed. Such receiving, transmitting and/or processing of data cannot practically be performed by the human mind on a large-scale within a reasonable period of time, such as within a second, a millisecond, microsecond, a real-time basis or other high speed required by the machines that generate the data, receive the data, convey the data, store the data and/or use the data.

One or more functions associated with the methods and/or processes described herein can require data to be manipulated in different ways within overlapping time spans. The human mind is not equipped to perform such different data manipulations independently, contemporaneously, in parallel, and/or on a coordinated basis within a reasonable period of time, such as within a second, a millisecond, microsecond, a real-time basis or other high speed required by the machines that generate the data, receive the data, convey the data, store the data and/or use the data.

One or more functions associated with the methods and/or processes described herein can be implemented in a system that is operable to electronically receive digital data via a wired or wireless communication network and/or to electronically transmit digital data via a wired or wireless communication network. Such receiving and transmitting cannot practically be performed by the human mind because the human mind is not equipped to electronically transmit or receive digital data, let alone to transmit and receive digital data via a wired or wireless communication network.

One or more functions associated with the methods and/or processes described herein can be implemented in a system that is operable to electronically store digital data in a memory device. Such storage cannot practically be performed by the human mind because the human mind is not equipped to electronically store digital data.

One or more functions associated with the methods and/or processes described herein may operate to cause an action by a processing module directly in response to a triggering event—without any intervening human interaction between the triggering event and the action. Any such actions may be identified as being performed “automatically”, “automatically based on” and/or “automatically in response to” such a triggering event. Furthermore, any such actions identified in such a fashion specifically preclude the operation of human activity with respect to these actions—even if the triggering event itself may be causally connected to a human activity of some kind.

While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations. 

What is claimed is:
 1. A method for execution in a storage network, the method comprises: performing a key derivation function on a first password to produce a key; issuing a set of second passwords based on the key to a set of storage units, wherein a data object is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices and wherein the set of encoded data slices are stored in the set of storage units; receiving a decode threshold number of confidential information responses from the set of storage units; regenerating a set of keys for the decode threshold number of confidential information responses; decrypting the confidential information responses using the set of keys to reproduce a set of encoded data slices; and dispersed storage error decoding the set of encoded data slices to produce recovered data.
 2. The method of claim 1, wherein a confidential information response of the decode threshold number of confidential information responses includes an associated pass key.
 3. The method of claim 2, wherein the regenerating is based on the associated pass key.
 4. The method of claim 1, wherein at least some passwords of the set of second passwords are blinded passwords.
 5. The method of claim 4, wherein the blinded passwords are generated using a password generator.
 6. The method of claim 5, wherein a confidential information response is generated based on a blinded password.
 7. The method of claim 1 wherein the decrypting the confidential information responses further comprises: decrypting an encrypted data slice 1 using a first key 1 to produce an encoded data slice 1 of the set of encoded data slices.
 8. The method of claim 1, wherein the key derivation function is adapted to require a minimum iteration count for generating the key.
 9. A computing device of a group of computing devices of a storage network, the computing device comprises: an interface; a local memory; and a processing module operably coupled to the interface and the local memory, wherein the processing module functions to: perform a key derivation function to produce a key; issue a set of passwords based on the key to a set of storage units, wherein a data object is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices and wherein the set of encoded data slices are stored in the set of storage units; receive a decode threshold number of confidential information responses from the set of storage units; regenerate a set of keys for the decode threshold number of confidential information responses; decrypt the confidential information responses using the set of keys to reproduce a set of encoded data slices; and dispersed storage error decode a decode threshold number of the set of encoded data slices to produce recovered data.
 10. The computing device of claim 9, wherein a confidential information response of the decode threshold number of confidential information responses includes an associated pass key.
 11. The computing device of claim 10, wherein the set of keys for the decode threshold number of confidential information responses are regenerated based on the associated pass key.
 12. The computing device of claim 9, wherein at least some passwords of the set of passwords are blinded passwords.
 13. The computing device of claim 12, wherein the blinded passwords are generated using a password generator.
 14. The computing device of claim 13, wherein a confidential information response is generated based on a blinded password.
 15. The computing device of claim 9, wherein the processing module further functions to: decrypt the confidential information responses by decrypting an encrypted data slice 1 using a first key 1 to produce an encoded data slice 1 of the set of encoded data slices.
 16. The computing device of claim 9, wherein the key derivation function is adapted to require a minimum iteration count for generating the key.
 17. A method for execution in a storage network, the method comprises: determining whether an iteration count threshold has been exceeded for generating a key; in response to a determination that the iteration count threshold has been exceeded, performing a key derivation function on a first password to produce a key; issuing a read threshold number of second passwords based on the key to a set of storage units, wherein a data object is dispersed error encoded in accordance with dispersed error encoding parameters to produce a set of encoded data slices and wherein the set of encoded data slices are stored in the set of storage units, wherein a read threshold number is number of encoded data slices per set to be read from storage for decoding a portion of the data object; receiving a decode threshold number of confidential information responses from the set of storage units, wherein a confidential information response of the decode threshold number of confidential information responses includes an encrypted encoded data slice and an associated passkey; regenerating a set of keys using the associated passkeys of the at least a decode threshold number of confidential information responses; decrypting the confidential information responses using the set of keys to reproduce a set of encoded data slices; and dispersed storage error decoding a decode threshold number of the set of encoded data slices to produce recovered data.
 18. The method of claim 17, wherein at least some passwords of the set of second passwords are blinded passwords.
 19. The method of claim 18, wherein the blinded passwords are generated using a password generator.
 20. The method of claim 18, wherein a confidential information response is generated based on a blinded password. 